diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..da6ab6cc8f333d7e89a99812866df8f24374d47c --- /dev/null +++ b/LICENSE @@ -0,0 +1,396 @@ +Attribution 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. Distribution of +Creative Commons public licenses does not create a lawyer-client or +other relationship. Creative Commons makes its licenses and related +information available on an "as-is" basis. Creative Commons gives no +warranties regarding its licenses, any material licensed under their +terms and conditions, or any related information. Creative Commons +disclaims all liability for damages resulting from their use to the +fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and +conditions that creators and other rights holders may use to share +original works of authorship and other material subject to copyright +and certain other rights specified in the public license below. The +following considerations are for informational purposes only, are not +exhaustive, and do not form part of our licenses. + + Considerations for licensors: Our public licenses are + intended for use by those authorized to give the public + permission to use material in ways otherwise restricted by + copyright and certain other rights. Our licenses are + irrevocable. Licensors should read and understand the terms + and conditions of the license they choose before applying it. + Licensors should also secure all rights necessary before + applying our licenses so that the public can reuse the + material as expected. Licensors should clearly mark any + material not subject to the license. This includes other CC- + licensed material, or material used under an exception or + limitation to copyright. More considerations for licensors: + wiki.creativecommons.org/Considerations_for_licensors + + Considerations for the public: By using one of our public + licenses, a licensor grants the public permission to use the + licensed material under specified terms and conditions. If + the licensor's permission is not necessary for any reason--for + example, because of any applicable exception or limitation to + copyright--then that use is not regulated by the license. Our + licenses grant only permissions under copyright and certain + other rights that a licensor has authority to grant. Use of + the licensed material may still be restricted for other + reasons, including because others have copyright or other + rights in the material. A licensor may make special requests, + such as asking that all changes be marked or described. + Although not required by our licenses, you are encouraged to + respect those requests where reasonable. More considerations + for the public: + wiki.creativecommons.org/Considerations_for_licensees + +======================================================================= + +Creative Commons Attribution 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree +to be bound by the terms and conditions of this Creative Commons +Attribution 4.0 International Public License ("Public License"). To the +extent this Public License may be interpreted as a contract, You are +granted the Licensed Rights in consideration of Your acceptance of +these terms and conditions, and the Licensor grants You such rights in +consideration of benefits the Licensor receives from making the +Licensed Material available under these terms and conditions. + + +Section 1 -- Definitions. + + a. Adapted Material means material subject to Copyright and Similar + Rights that is derived from or based upon the Licensed Material + and in which the Licensed Material is translated, altered, + arranged, transformed, or otherwise modified in a manner requiring + permission under the Copyright and Similar Rights held by the + Licensor. For purposes of this Public License, where the Licensed + Material is a musical work, performance, or sound recording, + Adapted Material is always produced where the Licensed Material is + synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright + and Similar Rights in Your contributions to Adapted Material in + accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights + closely related to copyright including, without limitation, + performance, broadcast, sound recording, and Sui Generis Database + Rights, without regard to how the rights are labeled or + categorized. For purposes of this Public License, the rights + specified in Section 2(b)(1)-(2) are not Copyright and Similar + Rights. + + d. Effective Technological Measures means those measures that, in the + absence of proper authority, may not be circumvented under laws + fulfilling obligations under Article 11 of the WIPO Copyright + Treaty adopted on December 20, 1996, and/or similar international + agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or + any other exception or limitation to Copyright and Similar Rights + that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, + or other material to which the Licensor applied this Public + License. + + g. Licensed Rights means the rights granted to You subject to the + terms and conditions of this Public License, which are limited to + all Copyright and Similar Rights that apply to Your use of the + Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights + under this Public License. + + i. Share means to provide material to the public by any means or + process that requires permission under the Licensed Rights, such + as reproduction, public display, public performance, distribution, + dissemination, communication, or importation, and to make material + available to the public including in ways that members of the + public may access the material from a place and at a time + individually chosen by them. + + j. Sui Generis Database Rights means rights other than copyright + resulting from Directive 96/9/EC of the European Parliament and of + the Council of 11 March 1996 on the legal protection of databases, + as amended and/or succeeded, as well as other essentially + equivalent rights anywhere in the world. + + k. You means the individual or entity exercising the Licensed Rights + under this Public License. Your has a corresponding meaning. + + +Section 2 -- Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, + the Licensor hereby grants You a worldwide, royalty-free, + non-sublicensable, non-exclusive, irrevocable license to + exercise the Licensed Rights in the Licensed Material to: + + a. reproduce and Share the Licensed Material, in whole or + in part; and + + b. produce, reproduce, and Share Adapted Material. + + 2. Exceptions and Limitations. For the avoidance of doubt, where + Exceptions and Limitations apply to Your use, this Public + License does not apply, and You do not need to comply with + its terms and conditions. + + 3. Term. The term of this Public License is specified in Section + 6(a). + + 4. Media and formats; technical modifications allowed. The + Licensor authorizes You to exercise the Licensed Rights in + all media and formats whether now known or hereafter created, + and to make technical modifications necessary to do so. The + Licensor waives and/or agrees not to assert any right or + authority to forbid You from making technical modifications + necessary to exercise the Licensed Rights, including + technical modifications necessary to circumvent Effective + Technological Measures. For purposes of this Public License, + simply making modifications authorized by this Section 2(a) + (4) never produces Adapted Material. + + 5. Downstream recipients. + + a. Offer from the Licensor -- Licensed Material. Every + recipient of the Licensed Material automatically + receives an offer from the Licensor to exercise the + Licensed Rights under the terms and conditions of this + Public License. + + b. No downstream restrictions. You may not offer or impose + any additional or different terms or conditions on, or + apply any Effective Technological Measures to, the + Licensed Material if doing so restricts exercise of the + Licensed Rights by any recipient of the Licensed + Material. + + 6. No endorsement. Nothing in this Public License constitutes or + may be construed as permission to assert or imply that You + are, or that Your use of the Licensed Material is, connected + with, or sponsored, endorsed, or granted official status by, + the Licensor or others designated to receive attribution as + provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not + licensed under this Public License, nor are publicity, + privacy, and/or other similar personality rights; however, to + the extent possible, the Licensor waives and/or agrees not to + assert any such rights held by the Licensor to the limited + extent necessary to allow You to exercise the Licensed + Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this + Public License. + + 3. To the extent possible, the Licensor waives any right to + collect royalties from You for the exercise of the Licensed + Rights, whether directly or through a collecting society + under any voluntary or waivable statutory or compulsory + licensing scheme. In all other cases the Licensor expressly + reserves any right to collect such royalties. + + +Section 3 -- License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the +following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified + form), You must: + + a. retain the following if it is supplied by the Licensor + with the Licensed Material: + + i. identification of the creator(s) of the Licensed + Material and any others designated to receive + attribution, in any reasonable manner requested by + the Licensor (including by pseudonym if + designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of + warranties; + + v. a URI or hyperlink to the Licensed Material to the + extent reasonably practicable; + + b. indicate if You modified the Licensed Material and + retain an indication of any previous modifications; and + + c. indicate the Licensed Material is licensed under this + Public License, and include the text of, or the URI or + hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any + reasonable manner based on the medium, means, and context in + which You Share the Licensed Material. For example, it may be + reasonable to satisfy the conditions by providing a URI or + hyperlink to a resource that includes the required + information. + + 3. If requested by the Licensor, You must remove any of the + information required by Section 3(a)(1)(A) to the extent + reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's + License You apply must not prevent recipients of the Adapted + Material from complying with this Public License. + + +Section 4 -- Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that +apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right + to extract, reuse, reproduce, and Share all or a substantial + portion of the contents of the database; + + b. if You include all or a substantial portion of the database + contents in a database in which You have Sui Generis Database + Rights, then the database in which You have Sui Generis Database + Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share + all or a substantial portion of the contents of the database. + +For the avoidance of doubt, this Section 4 supplements and does not +replace Your obligations under this Public License where the Licensed +Rights include other Copyright and Similar Rights. + + +Section 5 -- Disclaimer of Warranties and Limitation of Liability. + + a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE + EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS + AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF + ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, + IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, + WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR + PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, + ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT + KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT + ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU. + + b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE + TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, + NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, + INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, + COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR + USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN + ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR + DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR + IN PART, THIS LIMITATION MAY NOT APPLY TO YOU. + + c. The disclaimer of warranties and limitation of liability provided + above shall be interpreted in a manner that, to the extent + possible, most closely approximates an absolute disclaimer and + waiver of all liability. + + +Section 6 -- Term and Termination. + + a. This Public License applies for the term of the Copyright and + Similar Rights licensed here. However, if You fail to comply with + this Public License, then Your rights under this Public License + terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under + Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided + it is cured within 30 days of Your discovery of the + violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any + right the Licensor may have to seek remedies for Your violations + of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the + Licensed Material under separate terms or conditions or stop + distributing the Licensed Material at any time; however, doing so + will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public + License. + + +Section 7 -- Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different + terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the + Licensed Material not stated herein are separate from and + independent of the terms and conditions of this Public License. + + +Section 8 -- Interpretation. + + a. For the avoidance of doubt, this Public License does not, and + shall not be interpreted to, reduce, limit, restrict, or impose + conditions on any use of the Licensed Material that could lawfully + be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is + deemed unenforceable, it shall be automatically reformed to the + minimum extent necessary to make it enforceable. If the provision + cannot be reformed, it shall be severed from this Public License + without affecting the enforceability of the remaining terms and + conditions. + + c. No term or condition of this Public License will be waived and no + failure to comply consented to unless expressly agreed to by the + Licensor. + + d. Nothing in this Public License constitutes or may be interpreted + as a limitation upon, or waiver of, any privileges and immunities + that apply to the Licensor or You, including from the legal + processes of any jurisdiction or authority. + + +======================================================================= + +Creative Commons is not a party to its public +licenses. Notwithstanding, Creative Commons may elect to apply one of +its public licenses to material it publishes and in those instances +will be considered the “Licensor.” The text of the Creative Commons +public licenses is dedicated to the public domain under the CC0 Public +Domain Dedication. Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. + diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7827612fd84ee6c030046adb2f6ef4b4079a4266 --- /dev/null +++ b/README.md @@ -0,0 +1,127 @@ +--- +license: cc-by-4.0 +language: +- en +library_name: coreml +pipeline_tag: automatic-speech-recognition +base_model: +- ConwayResearch/Underdog-Bark-3.0 +- ConwayResearch/Underdog-Bark-3.0-Clean +tags: +- coreml +- apple-neural-engine +- speech-to-text +- asr +- parakeet +- underdog +- fluidaudio +- on-device +--- + +# Underdog Bark 3.0 · Core ML + +Full Core ML pipelines (preprocessor, encoder, decoder, joint) for +[Underdog Bark 3.0](https://huggingface.co/ConwayResearch/Underdog-Bark-3.0) and +[Underdog Bark 3.0 Clean](https://huggingface.co/ConwayResearch/Underdog-Bark-3.0-Clean) by Conway Research, laid +out in the [FluidAudio](https://github.com/FluidInference/FluidAudio) Parakeet TDT v3 component layout so they run +end to end in Swift with the encoder on the Apple Neural Engine. + +Underdog already ships a Core ML encoder for Bark 3.0 (with the preprocessor and decoder in Python), and Bark 3.0 Pro +as a full Core ML pipeline. This repo adds the rest of the pipeline for the small models, plus Bark 3.0 Clean, which +had no Core ML build. + +| Folder | Model | Output | +|---|---|---| +| [`bark-3.0/`](bark-3.0) | Underdog Bark 3.0 | Verbatim: keeps "um", "uh", repeats | +| [`bark-3.0-clean/`](bark-3.0-clean) | Underdog Bark 3.0 Clean | Cleaned: drops fillers, false starts and repeated words | + +English only. Tokenizer and output conventions (punctuation, casing, numerals) are Parakeet TDT v3's. + +## Files (each folder) + +| File | What it is | +|---|---| +| `Encoder.mlmodelc` | FastConformer encoder, 15 s window, **Neural Engine default** (sparse mask + 6-bit palette per 8 rows; 399 MB Bark 3.0, 410 MB Clean) | +| `Encoder_lut3.mlmodelc` | Same weights as a dense 3-bit palette per row (253 MB): **use this one on the GPU** (`.cpuAndGPU`) | +| `Decoder.mlmodelc` | 2-layer LSTM prediction network | +| `JointDecisionv3.mlmodelc` | TDT joint (token + duration), v3 interface | +| `Preprocessor.mlmodelc` | Parakeet TDT v3 mel front-end (Bark uses the original's) | +| `parakeet_vocab.json` | Parakeet TDT v3 vocabulary (8192 tokens) | +| `NOTICE` | Upstream attribution | + +### How the encoder is encoded + +Bark stores each encoder linear as per-row levels: 260 of the 264 matrices take five values per output row +`{0, ±a, ±b}`, and layer 23's four feed-forward matrices take seven. Core ML has no entropy-coded weight format, so +the weights are re-encoded as Core ML palettes (`constexpr_lut_to_sparse` + `constexpr_sparse_to_dense`, iOS 18): +the palette of every group of 8 output rows holds those rows' values, and each weight stores its index. Nothing is +re-quantized: every palettized weight reconstructs **bit-exactly** to the value in the upstream `model.bark` (240 +matrices; the 24 positional projections act on the fixed positional table and are folded into constants at export). + +Grouping 8 rows per palette matters on the Neural Engine, whose palette cost scales with the number of palettes: +per-row palettes run about 3× slower there. The GPU does not care, which is why `Encoder_lut3.mlmodelc` (per-row, +smallest dense file) is the GPU build; the sparse `Encoder.mlmodelc` is rebuilt on every GPU load (minutes), so do not +use it with `.cpuAndGPU`. + +## Benchmarks + +Apple M5 Pro, macOS 27, FluidAudio `asr-benchmark`, encoder on the Neural Engine, **full** LibriSpeech test sets, +corpus WER (total edits / total reference words, lowercase, punctuation stripped), RTFx = audio / processing time. + +| Model | test-clean WER | test-other WER | RTFx clean / other | +|---|--:|--:|--:| +| Bark 3.0 (this repo) | — | 4.96 % | — / 140× | +| Bark 3.0 Clean (this repo) | 2.37 %¹ | 4.66 % | 155×¹ / 143× | +| Bark 3.0 Clean, upstream MLX (`bark_mlx.py`, fp16), same files and scorer | 2.39 % | — | 55× / — | +| Parakeet TDT v3 (FluidInference, reference) | 2.27 % | 4.12 % | 150× / 140× | + +Long-form: a one-hour Earnings-22 recording (four concatenated calls) transcribes in **13.0 s (276×)** with Bark 3.0. + +¹ Measured with a dense 6-bit-palette build of the same exact encoder weights; the shipped sparse `Encoder.mlmodelc` +matched it on test-other (4.66 % both, 143× vs 142×). + +Notes: +- LibriSpeech references are verbatim and Clean removes words on purpose, so read speech is not where Clean shines; + see the upstream cards for AMI, DisfluencySpeech and the other Open ASR sets. +- Bark 3.0 is evaluated upstream with 0.25 s of leading silence. Through FluidAudio's sliding-window pipeline it makes + no difference (test-other 5.02 % with it vs 4.96 % without), so none is added. +- Bark models emit `` (token 0) where their training text had a quotation mark. Upstream decoders skip special + tokens; FluidAudio drops it from the transcript. If you write your own decoder, skip token 0 in the output text. + +## Usage (Swift, FluidAudio) + +Download one folder, then load it with the v3 component layout: + +```bash +hf download FluidInference/bark-3.0-coreml --include "bark-3.0/*" --local-dir bark +``` + +```swift +import FluidAudio + +let dir = URL(fileURLWithPath: "bark/bark-3.0") +let models = try AsrModels.loadLocal(from: dir, version: .ultra) // same contract as Parakeet TDT v3 +let asr = AsrManager(config: .default) +try await asr.loadModels(models) +var state = TdtDecoderState.make(decoderLayers: await asr.decoderLayerCount) +let result = try await asr.transcribe(URL(fileURLWithPath: "meeting.wav"), decoderState: &state) +print(result.text) +``` + +The first load compiles the encoder for the Neural Engine (up to a minute); later loads are cached by Core ML. +Requires iOS 18 / macOS 15 or later. Not yet tested on iPhone. + +## Credits and license + +- **Underdog Bark 3.0 and Bark 3.0 Clean** by Conway Research ([Underdog](https://underdog.ai)), CC-BY-4.0. +- Derived from **nvidia/parakeet-tdt-0.6b-v3** (© NVIDIA Corporation, CC-BY-4.0) through + **moondream/parakeet-ultra** (© Moondream, CC-BY-4.0). +- This repo: Core ML conversion and re-encoding by Fluid Inference. The encoder's linear layers are bit-exact to the + upstream weights; the remaining tensors (subsampling, depthwise convs, norms, decoder, joint) are the upstream values + converted to Core ML at fp16. `NOTICE` in each folder carries the upstream attribution. License: [CC-BY-4.0](LICENSE). + +## Changelog + +| Date | Files | Change | Issue | mobius PR | FluidAudio PR | HF commit | +|---|---|---|---|---|---|---| +| 2026-10-21 | `bark-3.0/*`, `bark-3.0-clean/*` | Initial release | — | — | — | (this commit) | diff --git a/bark-3.0-clean/Decoder.mlmodelc/analytics/coremldata.bin b/bark-3.0-clean/Decoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..031b5cec48dba8e042fd62d842b68df7d0066eb8 --- /dev/null +++ b/bark-3.0-clean/Decoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d00e8baa4b1d7fc164d7cbbb96ed294d531ba6fb684a1aa79f66d4e4fbdbd5da +size 243 diff --git a/bark-3.0-clean/Decoder.mlmodelc/coremldata.bin b/bark-3.0-clean/Decoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..d68fe320f706cb4f3cbd1399746ca18a14e40611 --- /dev/null +++ b/bark-3.0-clean/Decoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b33a57a008e7b138574bac1219037aadd7054caebfc42d0fa4adc97050f6a836 +size 569 diff --git a/bark-3.0-clean/Decoder.mlmodelc/metadata.json b/bark-3.0-clean/Decoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a5aa5f30a4a88234217d0925a68ac23179e00f00 --- /dev/null +++ b/bark-3.0-clean/Decoder.mlmodelc/metadata.json @@ -0,0 +1,123 @@ +[ + { + "metadataOutputVersion" : "3.0", + "shortDescription" : "Underdog Bark 3.0 Clean decoder (RNNT prediction network)", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 640 × 1)", + "shortDescription" : "", + "shape" : "[1, 640, 1]", + "name" : "decoder", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "h_out", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "c_out", + "type" : "MultiArray" + } + ], + "storagePrecision" : "Float16", + "modelParameters" : [ + + ], + "author" : "Fluid Inference", + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Select" : 1, + "Ios17.squeeze" : 4, + "Ios17.gather" : 1, + "Ios17.cast" : 8, + "Ios17.lstm" : 2, + "Split" : 2, + "Ios17.add" : 1, + "Ios17.transpose" : 2, + "Ios17.greaterEqual" : 1, + "Identity" : 1, + "Stack" : 2 + }, + "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1]", + "name" : "targets", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "target_length", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "h_in", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "c_in", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-10-21", + "com.github.apple.coremltools.source" : "torch==2.7.0", + "com.github.apple.coremltools.version" : "9.0b1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "Decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/bark-3.0-clean/Decoder.mlmodelc/model.mil b/bark-3.0-clean/Decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..1177d26b92ef8aa5b442604ee7ba03553ce1ca96 --- /dev/null +++ b/bark-3.0-clean/Decoder.mlmodelc/model.mil @@ -0,0 +1,73 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] +{ + func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { + tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; + tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; + tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; + tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; + tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_9")]; + tensor cast_1 = cast(dtype = cast_1_dtype_0, x = targets_to_int16)[name = tensor("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; + tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor(8193)]; + tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = tensor("add_2")]; + tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = tensor("select_0")]; + tensor y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor("y_cast_fp16_cast_uint16_axis_0"), val = tensor(0)]; + tensor select_0_to_int16_dtype_0 = const()[name = tensor("select_0_to_int16_dtype_0"), val = tensor("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor("cast_7")]; + tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; + tensor split_0_num_splits_0 = const()[name = tensor("split_0_num_splits_0"), val = tensor(2)]; + tensor split_0_axis_0 = const()[name = tensor("split_0_axis_0"), val = tensor(0)]; + tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_6")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = tensor("split_0_cast_fp16")]; + tensor split_1_num_splits_0 = const()[name = tensor("split_1_num_splits_0"), val = tensor(2)]; + tensor split_1_axis_0 = const()[name = tensor("split_1_axis_0"), val = tensor(0)]; + tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_5")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = tensor("split_1_cast_fp16")]; + tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_direction_0 = const()[name = tensor("input_lstm_layer_0_direction_0"), val = tensor("forward")]; + tensor input_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_lstm_layer_0_activation_0 = const()[name = tensor("input_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10487168)))]; + tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13764032)))]; + tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17040896)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor("transpose_2")]; + tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_lstm_layer_0_cast_fp16")]; + tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; + tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; + tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; + tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; + tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; + tensor concat_4_to_fp16 = const()[name = tensor("concat_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17046080)))]; + tensor concat_5_to_fp16 = const()[name = tensor("concat_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20322944)))]; + tensor concat_3_to_fp16 = const()[name = tensor("concat_3_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23599808)))]; + tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = tensor("input_cast_fp16")]; + tensor obj_3_axis_0 = const()[name = tensor("obj_3_axis_0"), val = tensor(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = tensor("obj_3_cast_fp16")]; + tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor obj_axis_0 = const()[name = tensor("obj_axis_0"), val = tensor(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = tensor("obj_cast_fp16")]; + tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; + tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; + tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_2")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_3")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_4")]; + tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; + } -> (decoder, h_out, c_out); +} \ No newline at end of file diff --git a/bark-3.0-clean/Decoder.mlmodelc/weights/weight.bin b/bark-3.0-clean/Decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..b0c6b6668798d720f56af5a147f2886955c2785c --- /dev/null +++ b/bark-3.0-clean/Decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6220cd08cb7eaacf5106da134c40f57e13783342c09f1be250dd31cc1b8b8a94 +size 23604992 diff --git a/bark-3.0-clean/Encoder.mlmodelc/analytics/coremldata.bin b/bark-3.0-clean/Encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..108b7481de19117da6ead55ee760366953cef8eb --- /dev/null +++ b/bark-3.0-clean/Encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f6763508e0d6caad8152bcc72cf8be1647f568c3cdc5c5ff9732c1c69e2734c +size 243 diff --git a/bark-3.0-clean/Encoder.mlmodelc/coremldata.bin b/bark-3.0-clean/Encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..899b21a4dd883ca0b9ce32cfc07db1d96a2116db --- /dev/null +++ b/bark-3.0-clean/Encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e08f511d23e5642bc45f6ecc0bc930e00f8f105d6a6c33fe03f83c8da9253070 +size 557 diff --git a/bark-3.0-clean/Encoder.mlmodelc/model.mil b/bark-3.0-clean/Encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..01f25acd040057a321e480a32255dc849d611222 --- /dev/null +++ b/bark-3.0-clean/Encoder.mlmodelc/model.mil @@ -0,0 +1,3644 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor mel, tensor mel_length) { + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + string var_86_to_fp16_dtype_0 = const()[name = string("op_86_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor mel_length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = mel_length)[name = string("cast_3")]; + tensor var_88_cast_fp16 = add(x = mel_length_to_fp16, y = var_87_promoted_to_fp16)[name = string("op_88_cast_fp16")]; + fp16 _inversed_90_y_0_to_fp16 = const()[name = string("_inversed_90_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = string("_inversed_90_cast_fp16")]; + fp16 var_91_to_fp16 = const()[name = string("op_91_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = string("lengths_1_cast_fp16")]; + tensor lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = string("lengths_3_cast_fp16")]; + fp16 var_95_promoted_to_fp16 = const()[name = string("op_95_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = string("op_96_cast_fp16")]; + fp16 _inversed_98_y_0_to_fp16 = const()[name = string("_inversed_98_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = string("_inversed_98_cast_fp16")]; + fp16 var_99_to_fp16 = const()[name = string("op_99_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = string("lengths_7_cast_fp16")]; + tensor lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = string("lengths_9_cast_fp16")]; + fp16 var_103_promoted_to_fp16 = const()[name = string("op_103_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = string("op_104_cast_fp16")]; + fp16 _inversed_106_y_0_to_fp16 = const()[name = string("_inversed_106_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = string("_inversed_106_cast_fp16")]; + fp16 var_107_to_fp16 = const()[name = string("op_107_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = string("lengths_13_cast_fp16")]; + tensor lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = string("lengths_cast_fp16")]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_2")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_315")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = string("input_1_cast_fp16")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([2, 2])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = string("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4736)))]; + tensor input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor([2, 2])]; + int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(256)]; + tensor input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = string("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5312)))]; + tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9984)))]; + tensor input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = string("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10560)))]; + tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141696)))]; + tensor input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")]; + tensor input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor([2, 2])]; + int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(256)]; + tensor input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = string("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142272)))]; + tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146944)))]; + tensor input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = string("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147520)))]; + tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278656)))]; + tensor input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_157_perm_0 = const()[name = string("op_157_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_158 = const()[name = string("op_158"), val = tensor([1, 188, -1])]; + tensor var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = string("transpose_314")]; + tensor input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor module_pre_encode_out_weight_to_fp16 = const()[name = string("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279232)))]; + tensor module_pre_encode_out_bias_to_fp16 = const()[name = string("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8667904)))]; + tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = string("linear_0_cast_fp16")]; + string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670016)))]; + tensor var_196_axes_0 = const()[name = string("op_196_axes_0"), val = tensor([-1])]; + tensor encoder_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = string("cast_1")]; + tensor var_196 = expand_dims(axes = var_196_axes_0, x = encoder_length)[name = string("op_196")]; + tensor pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = string("pad_mask_1")]; + tensor var_198_axes_0 = const()[name = string("op_198_axes_0"), val = tensor([1])]; + tensor var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = string("op_198")]; + tensor var_199 = const()[name = string("op_199"), val = tensor([1, 188, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = string("pad_mask_for_att_mask_1")]; + tensor var_201_perm_0 = const()[name = string("op_201_perm_0"), val = tensor([0, 2, 1])]; + tensor var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = string("pad_mask_for_att_mask")]; + tensor const_7 = const()[name = string("const_7"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = string("att_mask")]; + tensor mask_1 = logical_not(x = att_mask)[name = string("mask_1")]; + tensor pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")]; + tensor input_21_axes_0 = const()[name = string("input_21_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670848)))]; + tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8672960)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8675072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9199424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11432960))))[name = string("module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_1_bias_0_to_fp16 = const()[name = string("linear_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11498560)))]; + tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_21_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11506816))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12031168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14208448))))[name = string("module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14224896)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_25_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_232_to_fp16 = const()[name = string("op_232_to_fp16"), val = fp16(0x1p-1)]; + tensor var_233_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_232_to_fp16)[name = string("op_233_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_233_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14227008)))]; + tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14229120)))]; + tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = string("query_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14231232))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14362368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14910400))))[name = string("module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_249 = const()[name = string("op_249"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_249, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14926848))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15057984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15612352))))[name = string("module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_253, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15628800))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15759936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16311488))))[name = string("module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_257 = const()[name = string("op_257"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_257, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16327936)))]; + tensor var_269_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_269_cast_fp16")]; + tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16330048)))]; + tensor var_271_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_271_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor var_273_to_fp16 = const()[name = string("op_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16332160)))]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_271_cast_fp16)[name = string("transpose_312")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_273_to_fp16)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_281 = const()[name = string("op_281"), val = tensor([1, 8, -1, 188])]; + tensor x_11_cast_fp16 = reshape(shape = var_281, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_285_begin_0 = const()[name = string("op_285_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_285_end_0 = const()[name = string("op_285_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_285_end_mask_0 = const()[name = string("op_285_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_285_cast_fp16 = slice_by_index(begin = var_285_begin_0, end = var_285_end_0, end_mask = var_285_end_mask_0, x = x_11_cast_fp16)[name = string("op_285_cast_fp16")]; + tensor var_286 = const()[name = string("op_286"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_286, x = var_285_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_269_cast_fp16)[name = string("transpose_311")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_295_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_295_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_295_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_3_axes_0 = const()[name = string("mask_3_axes_0"), val = tensor([1])]; + tensor mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = string("mask_3")]; + fp16 var_12_to_fp16 = const()[name = string("op_12_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = string("scores_3_cast_fp16")]; + tensor var_301_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_301_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_301_cast_fp16, cond = mask_3)[name = string("input_33_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_305_perm_0 = const()[name = string("op_305_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_306 = const()[name = string("op_306"), val = tensor([1, -1, 1024])]; + tensor var_305_cast_fp16 = transpose(perm = var_305_perm_0, x = x_13_cast_fp16)[name = string("transpose_308")]; + tensor input_35_cast_fp16 = reshape(shape = var_306, x = var_305_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17100224))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17231360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17792704))))[name = string("module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17809152)))]; + tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17811264)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_41_perm_0 = const()[name = string("input_41_perm_0"), val = tensor([0, 2, 1])]; + string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")]; + tensor input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor([1])]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0])]; + tensor input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor([1])]; + int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17813376))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18075584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19190080))))[name = string("module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = string("transpose_307")]; + tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; + tensor var_328 = expand_dims(axes = var_328_axes_0, x = pad_mask)[name = string("op_328")]; + tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_328)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("constant")]; + fp16 const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1024)]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1])]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([0, 0])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([1])]; + tensor const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19222912)))]; + tensor const_249_to_fp16 = const()[name = string("const_249_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19241408)))]; + tensor input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19243520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19374656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19919104))))[name = string("module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_53_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_306")]; + tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor input_59_axes_0 = const()[name = string("input_59_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19935552)))]; + tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19937664)))]; + tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939776))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20464128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22709888))))[name = string("module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_59_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22775488))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23299840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25445184))))[name = string("module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_63_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_366_to_fp16 = const()[name = string("op_366_to_fp16"), val = fp16(0x1p-1)]; + tensor var_367_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_366_to_fp16)[name = string("op_367_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_367_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = string("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25461632)))]; + tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = string("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25463744)))]; + tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor input_73_axes_0 = const()[name = string("input_73_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25465856)))]; + tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25467968)))]; + tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25470080))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25994432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28160192))))[name = string("module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_73_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28225792))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28750144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30813888))))[name = string("module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_77_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p-1)]; + tensor var_396_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_396_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30830336)))]; + tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30832448)))]; + tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = string("query_3_cast_fp16")]; + tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30834560))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30965696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31525568))))[name = string("module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_412, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31542016))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31673152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32236288))))[name = string("module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_416 = const()[name = string("op_416"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_416, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32252736))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32383872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32941184))))[name = string("module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_420 = const()[name = string("op_420"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_420, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32957632)))]; + tensor var_432_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_432_cast_fp16")]; + tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32959744)))]; + tensor var_434_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_434_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_29_transpose_x_0 = const()[name = string("x_29_transpose_x_0"), val = bool(false)]; + bool x_29_transpose_y_0 = const()[name = string("x_29_transpose_y_0"), val = bool(false)]; + tensor var_436_to_fp16 = const()[name = string("op_436_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32961856)))]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_434_cast_fp16)[name = string("transpose_305")]; + tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_436_to_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_31_mode_0 = const()[name = string("x_31_mode_0"), val = string("constant")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_444 = const()[name = string("op_444"), val = tensor([1, 8, -1, 188])]; + tensor x_33_cast_fp16 = reshape(shape = var_444, x = x_31_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_448_begin_0 = const()[name = string("op_448_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_448_end_0 = const()[name = string("op_448_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_448_end_mask_0 = const()[name = string("op_448_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_448_cast_fp16 = slice_by_index(begin = var_448_begin_0, end = var_448_end_0, end_mask = var_448_end_mask_0, x = x_33_cast_fp16)[name = string("op_448_cast_fp16")]; + tensor var_449 = const()[name = string("op_449"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_449, x = var_448_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_432_cast_fp16)[name = string("transpose_304")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_458_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_458_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_458_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = string("scores_7_cast_fp16")]; + tensor var_464_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_464_cast_fp16, cond = mask_3)[name = string("input_85_cast_fp16")]; + bool x_35_transpose_x_0 = const()[name = string("x_35_transpose_x_0"), val = bool(false)]; + bool x_35_transpose_y_0 = const()[name = string("x_35_transpose_y_0"), val = bool(false)]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")]; + tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_468_perm_0 = const()[name = string("op_468_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_469 = const()[name = string("op_469"), val = tensor([1, -1, 1024])]; + tensor var_468_cast_fp16 = transpose(perm = var_468_perm_0, x = x_35_cast_fp16)[name = string("transpose_301")]; + tensor input_87_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33729920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33861056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34420288))))[name = string("module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_87_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34436736)))]; + tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34438848)))]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([1])]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([0, 0])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34440960))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35826240))))[name = string("module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = string("transpose_300")]; + tensor input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")]; + int32 x_41_split_num_splits_0 = const()[name = string("x_41_split_num_splits_0"), val = int32(2)]; + int32 x_41_split_axis_0 = const()[name = string("x_41_split_axis_0"), val = int32(1)]; + tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = string("x_41_split_cast_fp16")]; + tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = string("x_41_split_1_sigmoid_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_328)[name = string("input_97_cast_fp16")]; + tensor input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("constant")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1024)]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1])]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([0, 0])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1])]; + tensor const_250_to_fp16 = const()[name = string("const_250_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35859072)))]; + tensor const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35877568)))]; + tensor input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16, x = input_99_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_43_pad_type_0 = const()[name = string("x_43_pad_type_0"), val = string("valid")]; + tensor x_43_strides_0 = const()[name = string("x_43_strides_0"), val = tensor([1])]; + tensor x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor([0, 0])]; + tensor x_43_dilations_0 = const()[name = string("x_43_dilations_0"), val = tensor([1])]; + int32 x_43_groups_0 = const()[name = string("x_43_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35879680))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36010816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36557760))))[name = string("module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_105_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = string("transpose_299")]; + tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor input_111_axes_0 = const()[name = string("input_111_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36574208)))]; + tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36576320)))]; + tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36578432))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37102784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39308544))))[name = string("module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_111_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39374144))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39898496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41879360))))[name = string("module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_115_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1p-1)]; + tensor var_530_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_530_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor input_123_axes_0 = const()[name = string("input_123_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = string("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41895808)))]; + tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = string("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41897920)))]; + tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41900032)))]; + tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41902144)))]; + tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41904256))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42428608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44654656))))[name = string("module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_125_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44720256))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45244608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47358400))))[name = string("module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_129_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_558_to_fp16 = const()[name = string("op_558_to_fp16"), val = fp16(0x1p-1)]; + tensor var_559_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_558_to_fp16)[name = string("op_559_cast_fp16")]; + tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_559_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47374848)))]; + tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47376960)))]; + tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = string("query_5_cast_fp16")]; + tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47379072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47510208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48067648))))[name = string("module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_575 = const()[name = string("op_575"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_575, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084096))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48215232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48779456))))[name = string("module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_579 = const()[name = string("op_579"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_579, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48795904))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48927040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49490560))))[name = string("module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_583 = const()[name = string("op_583"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_583, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49507008)))]; + tensor var_595_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_595_cast_fp16")]; + tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49509120)))]; + tensor var_597_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_597_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_51_transpose_x_0 = const()[name = string("x_51_transpose_x_0"), val = bool(false)]; + bool x_51_transpose_y_0 = const()[name = string("x_51_transpose_y_0"), val = bool(false)]; + tensor var_599_to_fp16 = const()[name = string("op_599_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49511232)))]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_597_cast_fp16)[name = string("transpose_298")]; + tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_599_to_fp16)[name = string("x_51_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_607 = const()[name = string("op_607"), val = tensor([1, 8, -1, 188])]; + tensor x_55_cast_fp16 = reshape(shape = var_607, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_611_begin_0 = const()[name = string("op_611_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_611_end_0 = const()[name = string("op_611_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_611_end_mask_0 = const()[name = string("op_611_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_611_cast_fp16 = slice_by_index(begin = var_611_begin_0, end = var_611_end_0, end_mask = var_611_end_mask_0, x = x_55_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor var_612 = const()[name = string("op_612"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_612, x = var_611_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_595_cast_fp16)[name = string("transpose_297")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_621_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_621_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_621_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = string("scores_11_cast_fp16")]; + tensor var_627_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_627_cast_fp16, cond = mask_3)[name = string("input_137_cast_fp16")]; + bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)]; + bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)]; + tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")]; + tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_631_perm_0 = const()[name = string("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_632 = const()[name = string("op_632"), val = tensor([1, -1, 1024])]; + tensor var_631_cast_fp16 = transpose(perm = var_631_perm_0, x = x_57_cast_fp16)[name = string("transpose_294")]; + tensor input_139_cast_fp16 = reshape(shape = var_632, x = var_631_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50279296))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50410432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50972544))))[name = string("module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_139_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50988992)))]; + tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50991104)))]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50993216))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51255424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52376448))))[name = string("module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = string("transpose_293")]; + tensor input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_145_cast_fp16)[name = string("input_147_cast_fp16")]; + int32 x_63_split_num_splits_0 = const()[name = string("x_63_split_num_splits_0"), val = int32(2)]; + int32 x_63_split_axis_0 = const()[name = string("x_63_split_axis_0"), val = int32(1)]; + tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = string("x_63_split_cast_fp16")]; + tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = string("x_63_split_1_sigmoid_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_328)[name = string("input_149_cast_fp16")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_151_mode_0 = const()[name = string("input_151_mode_0"), val = string("constant")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1024)]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + tensor const_252_to_fp16 = const()[name = string("const_252_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52409280)))]; + tensor const_253_to_fp16 = const()[name = string("const_253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52427776)))]; + tensor input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16, x = input_151_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("valid")]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([0, 0])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52429888))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52561024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53111360))))[name = string("module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_157_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = string("transpose_292")]; + tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = string("input_161_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53127808)))]; + tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53129920)))]; + tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53132032))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53656384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55876160))))[name = string("module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_163_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55941760))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56466112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58491584))))[name = string("module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_167_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_692_to_fp16 = const()[name = string("op_692_to_fp16"), val = fp16(0x1p-1)]; + tensor var_693_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_692_to_fp16)[name = string("op_693_cast_fp16")]; + tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_693_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor input_175_axes_0 = const()[name = string("input_175_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = string("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58508032)))]; + tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = string("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58510144)))]; + tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor input_177_axes_0 = const()[name = string("input_177_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58512256)))]; + tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58514368)))]; + tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58516480))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59040832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61258240))))[name = string("module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_177_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61323840))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61848192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63890304))))[name = string("module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_181_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_721_to_fp16 = const()[name = string("op_721_to_fp16"), val = fp16(0x1p-1)]; + tensor var_722_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_721_to_fp16)[name = string("op_722_cast_fp16")]; + tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_722_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63906752)))]; + tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63908864)))]; + tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = string("query_7_cast_fp16")]; + tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63910976))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64042112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64598912))))[name = string("module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_738, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64615360))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64746496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65308032))))[name = string("module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_742, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65324480))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65455616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66015296))))[name = string("module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66031744)))]; + tensor var_758_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_758_cast_fp16")]; + tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66033856)))]; + tensor var_760_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_760_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_73_transpose_x_0 = const()[name = string("x_73_transpose_x_0"), val = bool(false)]; + bool x_73_transpose_y_0 = const()[name = string("x_73_transpose_y_0"), val = bool(false)]; + tensor var_762_to_fp16 = const()[name = string("op_762_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66035968)))]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_760_cast_fp16)[name = string("transpose_291")]; + tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_762_to_fp16)[name = string("x_73_cast_fp16")]; + tensor x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_770 = const()[name = string("op_770"), val = tensor([1, 8, -1, 188])]; + tensor x_77_cast_fp16 = reshape(shape = var_770, x = x_75_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_774_begin_0 = const()[name = string("op_774_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_774_end_0 = const()[name = string("op_774_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_774_end_mask_0 = const()[name = string("op_774_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = x_77_cast_fp16)[name = string("op_774_cast_fp16")]; + tensor var_775 = const()[name = string("op_775"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_758_cast_fp16)[name = string("transpose_290")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_784_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_784_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_784_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = string("scores_15_cast_fp16")]; + tensor var_790_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = string("op_790_cast_fp16")]; + tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_790_cast_fp16, cond = mask_3)[name = string("input_189_cast_fp16")]; + bool x_79_transpose_x_0 = const()[name = string("x_79_transpose_x_0"), val = bool(false)]; + bool x_79_transpose_y_0 = const()[name = string("x_79_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")]; + tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor var_794_perm_0 = const()[name = string("op_794_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_795 = const()[name = string("op_795"), val = tensor([1, -1, 1024])]; + tensor var_794_cast_fp16 = transpose(perm = var_794_perm_0, x = x_79_cast_fp16)[name = string("transpose_287")]; + tensor input_191_cast_fp16 = reshape(shape = var_795, x = var_794_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66804032))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66935168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67494976))))[name = string("module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_191_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor x_83_axes_0 = const()[name = string("x_83_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67511424)))]; + tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67513536)))]; + tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_197_perm_0 = const()[name = string("input_197_perm_0"), val = tensor([0, 2, 1])]; + string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("valid")]; + tensor input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor([1])]; + tensor input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor([0, 0])]; + tensor input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor([1])]; + int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67515648))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67777856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68897856))))[name = string("module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = string("transpose_286")]; + tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_197_cast_fp16)[name = string("input_199_cast_fp16")]; + int32 x_85_split_num_splits_0 = const()[name = string("x_85_split_num_splits_0"), val = int32(2)]; + int32 x_85_split_axis_0 = const()[name = string("x_85_split_axis_0"), val = int32(1)]; + tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = string("x_85_split_cast_fp16")]; + tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = string("x_85_split_1_sigmoid_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_328)[name = string("input_201_cast_fp16")]; + tensor input_203_pad_0 = const()[name = string("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("constant")]; + fp16 const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = fp16(0x0p+0)]; + tensor input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = string("input_203_cast_fp16")]; + string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("valid")]; + int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1024)]; + tensor input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor([1])]; + tensor input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor([0, 0])]; + tensor input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor([1])]; + tensor const_254_to_fp16 = const()[name = string("const_254_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68930688)))]; + tensor const_255_to_fp16 = const()[name = string("const_255_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68949184)))]; + tensor input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16, x = input_203_cast_fp16)[name = string("input_207_cast_fp16")]; + tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = string("input_209_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("valid")]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68951296))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69082432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632704))))[name = string("module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_209_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = string("transpose_285")]; + tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + tensor input_215_axes_0 = const()[name = string("input_215_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69649152)))]; + tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69651264)))]; + tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69653376))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70177728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72391168))))[name = string("module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_215_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72456768))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72981120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74939776))))[name = string("module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_219_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)]; + tensor var_856_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_856_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor input_227_axes_0 = const()[name = string("input_227_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = string("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74956224)))]; + tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = string("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74958336)))]; + tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = string("input_227_cast_fp16")]; + tensor input_229_axes_0 = const()[name = string("input_229_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74960448)))]; + tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74962560)))]; + tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74964672))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75489024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77702976))))[name = string("module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_229_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77768576))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78292928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80301504))))[name = string("module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_233_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p-1)]; + tensor var_885_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_885_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80317952)))]; + tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80320064)))]; + tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = string("query_9_cast_fp16")]; + tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80322176))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80453312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81010048))))[name = string("module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_901 = const()[name = string("op_901"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_901, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81026496))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81157632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81718656))))[name = string("module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_905 = const()[name = string("op_905"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_905, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81735104))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81866240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82426432))))[name = string("module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_909 = const()[name = string("op_909"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_909, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82442880)))]; + tensor var_921_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_921_cast_fp16")]; + tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82444992)))]; + tensor var_923_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_923_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_95_transpose_x_0 = const()[name = string("x_95_transpose_x_0"), val = bool(false)]; + bool x_95_transpose_y_0 = const()[name = string("x_95_transpose_y_0"), val = bool(false)]; + tensor var_925_to_fp16 = const()[name = string("op_925_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82447104)))]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_923_cast_fp16)[name = string("transpose_284")]; + tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_925_to_fp16)[name = string("x_95_cast_fp16")]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_97_mode_0 = const()[name = string("x_97_mode_0"), val = string("constant")]; + fp16 const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = fp16(0x0p+0)]; + tensor x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_933 = const()[name = string("op_933"), val = tensor([1, 8, -1, 188])]; + tensor x_99_cast_fp16 = reshape(shape = var_933, x = x_97_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor var_937_begin_0 = const()[name = string("op_937_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_937_end_0 = const()[name = string("op_937_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_937_end_mask_0 = const()[name = string("op_937_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_937_cast_fp16 = slice_by_index(begin = var_937_begin_0, end = var_937_end_0, end_mask = var_937_end_mask_0, x = x_99_cast_fp16)[name = string("op_937_cast_fp16")]; + tensor var_938 = const()[name = string("op_938"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_938, x = var_937_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_921_cast_fp16)[name = string("transpose_283")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_947_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_947_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_947_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = string("scores_19_cast_fp16")]; + tensor var_953_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = string("op_953_cast_fp16")]; + tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_953_cast_fp16, cond = mask_3)[name = string("input_241_cast_fp16")]; + bool x_101_transpose_x_0 = const()[name = string("x_101_transpose_x_0"), val = bool(false)]; + bool x_101_transpose_y_0 = const()[name = string("x_101_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")]; + tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_957_perm_0 = const()[name = string("op_957_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_958 = const()[name = string("op_958"), val = tensor([1, -1, 1024])]; + tensor var_957_cast_fp16 = transpose(perm = var_957_perm_0, x = x_101_cast_fp16)[name = string("transpose_280")]; + tensor input_243_cast_fp16 = reshape(shape = var_958, x = var_957_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83215168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83346304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83909952))))[name = string("module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_243_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = string("input_247_cast_fp16")]; + tensor x_105_axes_0 = const()[name = string("x_105_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83926400)))]; + tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83928512)))]; + tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor input_249_perm_0 = const()[name = string("input_249_perm_0"), val = tensor([0, 2, 1])]; + string input_251_pad_type_0 = const()[name = string("input_251_pad_type_0"), val = string("valid")]; + tensor input_251_strides_0 = const()[name = string("input_251_strides_0"), val = tensor([1])]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0])]; + tensor input_251_dilations_0 = const()[name = string("input_251_dilations_0"), val = tensor([1])]; + int32 input_251_groups_0 = const()[name = string("input_251_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83930624))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84192832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85315968))))[name = string("module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = string("transpose_279")]; + tensor input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + int32 x_107_split_num_splits_0 = const()[name = string("x_107_split_num_splits_0"), val = int32(2)]; + int32 x_107_split_axis_0 = const()[name = string("x_107_split_axis_0"), val = int32(1)]; + tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = string("x_107_split_cast_fp16")]; + tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = string("x_107_split_1_sigmoid_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_328)[name = string("input_253_cast_fp16")]; + tensor input_255_pad_0 = const()[name = string("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("constant")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = string("input_255_cast_fp16")]; + string input_257_pad_type_0 = const()[name = string("input_257_pad_type_0"), val = string("valid")]; + int32 input_257_groups_0 = const()[name = string("input_257_groups_0"), val = int32(1024)]; + tensor input_257_strides_0 = const()[name = string("input_257_strides_0"), val = tensor([1])]; + tensor input_257_pad_0 = const()[name = string("input_257_pad_0"), val = tensor([0, 0])]; + tensor input_257_dilations_0 = const()[name = string("input_257_dilations_0"), val = tensor([1])]; + tensor const_256_to_fp16 = const()[name = string("const_256_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85348800)))]; + tensor const_257_to_fp16 = const()[name = string("const_257_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85367296)))]; + tensor input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16, x = input_255_cast_fp16)[name = string("input_259_cast_fp16")]; + tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = string("input_261_cast_fp16")]; + string x_109_pad_type_0 = const()[name = string("x_109_pad_type_0"), val = string("valid")]; + tensor x_109_strides_0 = const()[name = string("x_109_strides_0"), val = tensor([1])]; + tensor x_109_pad_0 = const()[name = string("x_109_pad_0"), val = tensor([0, 0])]; + tensor x_109_dilations_0 = const()[name = string("x_109_dilations_0"), val = tensor([1])]; + int32 x_109_groups_0 = const()[name = string("x_109_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85369408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85500544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86053760))))[name = string("module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_261_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = string("transpose_278")]; + tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor input_267_axes_0 = const()[name = string("input_267_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86070208)))]; + tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86072320)))]; + tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86074432))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86598784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88811456))))[name = string("module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_267_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88877056))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89401408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91346176))))[name = string("module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_271_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1019_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1019_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor input_279_axes_0 = const()[name = string("input_279_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = string("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91362624)))]; + tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = string("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91364736)))]; + tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = string("input_279_cast_fp16")]; + tensor input_281_axes_0 = const()[name = string("input_281_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91366848)))]; + tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91368960)))]; + tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = string("input_281_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91371072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91895424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94113280))))[name = string("module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_281_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94178880))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94703232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96752896))))[name = string("module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_285_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1047_to_fp16 = const()[name = string("op_1047_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1048_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1047_to_fp16)[name = string("op_1048_cast_fp16")]; + tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1048_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96769344)))]; + tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96771456)))]; + tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = string("query_11_cast_fp16")]; + tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96773568))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96904704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97459776))))[name = string("module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1064 = const()[name = string("op_1064"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1064, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97476224))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97607360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98170432))))[name = string("module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1068 = const()[name = string("op_1068"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1068, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98186880))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98318016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98877888))))[name = string("module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1072 = const()[name = string("op_1072"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1072, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98894336)))]; + tensor var_1084_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1084_cast_fp16")]; + tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98896448)))]; + tensor var_1086_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1086_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98898560)))]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1086_cast_fp16)[name = string("transpose_277")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1088_to_fp16)[name = string("x_117_cast_fp16")]; + tensor x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor var_1096 = const()[name = string("op_1096"), val = tensor([1, 8, -1, 188])]; + tensor x_121_cast_fp16 = reshape(shape = var_1096, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor var_1100_begin_0 = const()[name = string("op_1100_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1100_end_0 = const()[name = string("op_1100_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1100_end_mask_0 = const()[name = string("op_1100_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1100_cast_fp16 = slice_by_index(begin = var_1100_begin_0, end = var_1100_end_0, end_mask = var_1100_end_mask_0, x = x_121_cast_fp16)[name = string("op_1100_cast_fp16")]; + tensor var_1101 = const()[name = string("op_1101"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1101, x = var_1100_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1084_cast_fp16)[name = string("transpose_276")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1110_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1110_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1110_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = string("scores_23_cast_fp16")]; + tensor var_1116_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = string("op_1116_cast_fp16")]; + tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1116_cast_fp16, cond = mask_3)[name = string("input_293_cast_fp16")]; + bool x_123_transpose_x_0 = const()[name = string("x_123_transpose_x_0"), val = bool(false)]; + bool x_123_transpose_y_0 = const()[name = string("x_123_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")]; + tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1120_perm_0 = const()[name = string("op_1120_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1121 = const()[name = string("op_1121"), val = tensor([1, -1, 1024])]; + tensor var_1120_cast_fp16 = transpose(perm = var_1120_perm_0, x = x_123_cast_fp16)[name = string("transpose_273")]; + tensor input_295_cast_fp16 = reshape(shape = var_1121, x = var_1120_cast_fp16)[name = string("input_295_cast_fp16")]; + tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99666624))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99797760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100358528))))[name = string("module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_295_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = string("input_299_cast_fp16")]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100374976)))]; + tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100377088)))]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_301_perm_0 = const()[name = string("input_301_perm_0"), val = tensor([0, 2, 1])]; + string input_303_pad_type_0 = const()[name = string("input_303_pad_type_0"), val = string("valid")]; + tensor input_303_strides_0 = const()[name = string("input_303_strides_0"), val = tensor([1])]; + tensor input_303_pad_0 = const()[name = string("input_303_pad_0"), val = tensor([0, 0])]; + tensor input_303_dilations_0 = const()[name = string("input_303_dilations_0"), val = tensor([1])]; + int32 input_303_groups_0 = const()[name = string("input_303_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100379200))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100641408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101765568))))[name = string("module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = string("transpose_272")]; + tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_301_cast_fp16)[name = string("input_303_cast_fp16")]; + int32 x_129_split_num_splits_0 = const()[name = string("x_129_split_num_splits_0"), val = int32(2)]; + int32 x_129_split_axis_0 = const()[name = string("x_129_split_axis_0"), val = int32(1)]; + tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = string("x_129_split_cast_fp16")]; + tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = string("x_129_split_1_sigmoid_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_328)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("constant")]; + fp16 const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string input_309_pad_type_0 = const()[name = string("input_309_pad_type_0"), val = string("valid")]; + int32 input_309_groups_0 = const()[name = string("input_309_groups_0"), val = int32(1024)]; + tensor input_309_strides_0 = const()[name = string("input_309_strides_0"), val = tensor([1])]; + tensor input_309_pad_0 = const()[name = string("input_309_pad_0"), val = tensor([0, 0])]; + tensor input_309_dilations_0 = const()[name = string("input_309_dilations_0"), val = tensor([1])]; + tensor const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101798400)))]; + tensor const_259_to_fp16 = const()[name = string("const_259_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101816896)))]; + tensor input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16, x = input_307_cast_fp16)[name = string("input_311_cast_fp16")]; + tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = string("input_313_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("valid")]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([0, 0])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101819008))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101950144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102502912))))[name = string("module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_313_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = string("transpose_271")]; + tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_axes_0 = const()[name = string("input_319_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102519360)))]; + tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102521472)))]; + tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102523584))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103047936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105258752))))[name = string("module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_319_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105324352))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105848704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107931008))))[name = string("module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_323_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1182_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1181_to_fp16)[name = string("op_1182_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1182_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_axes_0 = const()[name = string("input_331_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = string("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107947456)))]; + tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = string("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107949568)))]; + tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + tensor input_333_axes_0 = const()[name = string("input_333_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107951680)))]; + tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107953792)))]; + tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = string("input_333_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107955904))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108480256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110696320))))[name = string("module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_333_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110761920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111286272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113342592))))[name = string("module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_337_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1210_to_fp16 = const()[name = string("op_1210_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1211_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1210_to_fp16)[name = string("op_1211_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1211_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113359040)))]; + tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113361152)))]; + tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = string("query_13_cast_fp16")]; + tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113363264))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113494400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114049024))))[name = string("module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1227 = const()[name = string("op_1227"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1227, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114065472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114196608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114758720))))[name = string("module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1231 = const()[name = string("op_1231"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1231, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114775168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114906304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115467456))))[name = string("module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1235 = const()[name = string("op_1235"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1235, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115483904)))]; + tensor var_1247_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1247_cast_fp16")]; + tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115486016)))]; + tensor var_1249_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1249_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_139_transpose_x_0 = const()[name = string("x_139_transpose_x_0"), val = bool(false)]; + bool x_139_transpose_y_0 = const()[name = string("x_139_transpose_y_0"), val = bool(false)]; + tensor var_1251_to_fp16 = const()[name = string("op_1251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115488128)))]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1249_cast_fp16)[name = string("transpose_270")]; + tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1251_to_fp16)[name = string("x_139_cast_fp16")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_141_mode_0 = const()[name = string("x_141_mode_0"), val = string("constant")]; + fp16 const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = fp16(0x0p+0)]; + tensor x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1259 = const()[name = string("op_1259"), val = tensor([1, 8, -1, 188])]; + tensor x_143_cast_fp16 = reshape(shape = var_1259, x = x_141_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1263_begin_0 = const()[name = string("op_1263_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1263_end_0 = const()[name = string("op_1263_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1263_end_mask_0 = const()[name = string("op_1263_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1263_cast_fp16 = slice_by_index(begin = var_1263_begin_0, end = var_1263_end_0, end_mask = var_1263_end_mask_0, x = x_143_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor var_1264 = const()[name = string("op_1264"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1264, x = var_1263_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1247_cast_fp16)[name = string("transpose_269")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1273_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1273_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1273_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = string("scores_27_cast_fp16")]; + tensor var_1279_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = string("op_1279_cast_fp16")]; + tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1279_cast_fp16, cond = mask_3)[name = string("input_345_cast_fp16")]; + bool x_145_transpose_x_0 = const()[name = string("x_145_transpose_x_0"), val = bool(false)]; + bool x_145_transpose_y_0 = const()[name = string("x_145_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")]; + tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1283_perm_0 = const()[name = string("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1284 = const()[name = string("op_1284"), val = tensor([1, -1, 1024])]; + tensor var_1283_cast_fp16 = transpose(perm = var_1283_perm_0, x = x_145_cast_fp16)[name = string("transpose_266")]; + tensor input_347_cast_fp16 = reshape(shape = var_1284, x = var_1283_cast_fp16)[name = string("input_347_cast_fp16")]; + tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116256192))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116387328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116951552))))[name = string("module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_347_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = string("input_351_cast_fp16")]; + tensor x_149_axes_0 = const()[name = string("x_149_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116968000)))]; + tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116970112)))]; + tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + string input_355_pad_type_0 = const()[name = string("input_355_pad_type_0"), val = string("valid")]; + tensor input_355_strides_0 = const()[name = string("input_355_strides_0"), val = tensor([1])]; + tensor input_355_pad_0 = const()[name = string("input_355_pad_0"), val = tensor([0, 0])]; + tensor input_355_dilations_0 = const()[name = string("input_355_dilations_0"), val = tensor([1])]; + int32 input_355_groups_0 = const()[name = string("input_355_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116972224))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117234432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118357504))))[name = string("module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = string("transpose_265")]; + tensor input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_353_cast_fp16)[name = string("input_355_cast_fp16")]; + int32 x_151_split_num_splits_0 = const()[name = string("x_151_split_num_splits_0"), val = int32(2)]; + int32 x_151_split_axis_0 = const()[name = string("x_151_split_axis_0"), val = int32(1)]; + tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = string("x_151_split_cast_fp16")]; + tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = string("x_151_split_1_sigmoid_cast_fp16")]; + tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_328)[name = string("input_357_cast_fp16")]; + tensor input_359_pad_0 = const()[name = string("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_359_mode_0 = const()[name = string("input_359_mode_0"), val = string("constant")]; + fp16 const_77_to_fp16 = const()[name = string("const_77_to_fp16"), val = fp16(0x0p+0)]; + tensor input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = string("input_359_cast_fp16")]; + string input_361_pad_type_0 = const()[name = string("input_361_pad_type_0"), val = string("valid")]; + int32 input_361_groups_0 = const()[name = string("input_361_groups_0"), val = int32(1024)]; + tensor input_361_strides_0 = const()[name = string("input_361_strides_0"), val = tensor([1])]; + tensor input_361_pad_0 = const()[name = string("input_361_pad_0"), val = tensor([0, 0])]; + tensor input_361_dilations_0 = const()[name = string("input_361_dilations_0"), val = tensor([1])]; + tensor const_260_to_fp16 = const()[name = string("const_260_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118390336)))]; + tensor const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118408832)))]; + tensor input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16, x = input_359_cast_fp16)[name = string("input_363_cast_fp16")]; + tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = string("input_365_cast_fp16")]; + string x_153_pad_type_0 = const()[name = string("x_153_pad_type_0"), val = string("valid")]; + tensor x_153_strides_0 = const()[name = string("x_153_strides_0"), val = tensor([1])]; + tensor x_153_pad_0 = const()[name = string("x_153_pad_0"), val = tensor([0, 0])]; + tensor x_153_dilations_0 = const()[name = string("x_153_dilations_0"), val = tensor([1])]; + int32 x_153_groups_0 = const()[name = string("x_153_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118410944))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118542080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119094400))))[name = string("module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_365_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = string("transpose_264")]; + tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + tensor input_371_axes_0 = const()[name = string("input_371_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119110848)))]; + tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119112960)))]; + tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119115072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119639424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121850496))))[name = string("module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_371_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121916096))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122440448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124590528))))[name = string("module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_375_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1345_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1345_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor input_383_axes_0 = const()[name = string("input_383_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = string("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124606976)))]; + tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = string("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124609088)))]; + tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = string("input_383_cast_fp16")]; + tensor input_385_axes_0 = const()[name = string("input_385_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124611200)))]; + tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124613312)))]; + tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = string("input_385_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124615424))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125139776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127348032))))[name = string("module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_385_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127413632))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127937984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130026432))))[name = string("module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_389_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1373_to_fp16 = const()[name = string("op_1373_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1374_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1373_to_fp16)[name = string("op_1374_cast_fp16")]; + tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1374_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130042880)))]; + tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130044992)))]; + tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = string("query_15_cast_fp16")]; + tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130047104))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130178240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130728576))))[name = string("module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1390 = const()[name = string("op_1390"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1390, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130745024))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130876160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131436992))))[name = string("module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1394 = const()[name = string("op_1394"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1394, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131453440))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131584576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132136512))))[name = string("module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1398 = const()[name = string("op_1398"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1398, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132152960)))]; + tensor var_1410_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1410_cast_fp16")]; + tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132155072)))]; + tensor var_1412_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1412_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_161_transpose_x_0 = const()[name = string("x_161_transpose_x_0"), val = bool(false)]; + bool x_161_transpose_y_0 = const()[name = string("x_161_transpose_y_0"), val = bool(false)]; + tensor var_1414_to_fp16 = const()[name = string("op_1414_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132157184)))]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1412_cast_fp16)[name = string("transpose_263")]; + tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1414_to_fp16)[name = string("x_161_cast_fp16")]; + tensor x_163_pad_0 = const()[name = string("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_163_mode_0 = const()[name = string("x_163_mode_0"), val = string("constant")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor var_1422 = const()[name = string("op_1422"), val = tensor([1, 8, -1, 188])]; + tensor x_165_cast_fp16 = reshape(shape = var_1422, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1426_begin_0 = const()[name = string("op_1426_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1426_end_0 = const()[name = string("op_1426_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1426_end_mask_0 = const()[name = string("op_1426_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = x_165_cast_fp16)[name = string("op_1426_cast_fp16")]; + tensor var_1427 = const()[name = string("op_1427"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1427, x = var_1426_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1410_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_1436_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1436_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_1436_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = string("scores_31_cast_fp16")]; + tensor var_1442_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = string("op_1442_cast_fp16")]; + tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1442_cast_fp16, cond = mask_3)[name = string("input_397_cast_fp16")]; + bool x_167_transpose_x_0 = const()[name = string("x_167_transpose_x_0"), val = bool(false)]; + bool x_167_transpose_y_0 = const()[name = string("x_167_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")]; + tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1446_perm_0 = const()[name = string("op_1446_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1447 = const()[name = string("op_1447"), val = tensor([1, -1, 1024])]; + tensor var_1446_cast_fp16 = transpose(perm = var_1446_perm_0, x = x_167_cast_fp16)[name = string("transpose_259")]; + tensor input_399_cast_fp16 = reshape(shape = var_1447, x = var_1446_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132925248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133056384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133617344))))[name = string("module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_399_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = string("input_403_cast_fp16")]; + tensor x_171_axes_0 = const()[name = string("x_171_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133633792)))]; + tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133635904)))]; + tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string input_407_pad_type_0 = const()[name = string("input_407_pad_type_0"), val = string("valid")]; + tensor input_407_strides_0 = const()[name = string("input_407_strides_0"), val = tensor([1])]; + tensor input_407_pad_0 = const()[name = string("input_407_pad_0"), val = tensor([0, 0])]; + tensor input_407_dilations_0 = const()[name = string("input_407_dilations_0"), val = tensor([1])]; + int32 input_407_groups_0 = const()[name = string("input_407_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133638016))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133900224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135023680))))[name = string("module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = string("transpose_258")]; + tensor input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_405_cast_fp16)[name = string("input_407_cast_fp16")]; + int32 x_173_split_num_splits_0 = const()[name = string("x_173_split_num_splits_0"), val = int32(2)]; + int32 x_173_split_axis_0 = const()[name = string("x_173_split_axis_0"), val = int32(1)]; + tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = string("x_173_split_cast_fp16")]; + tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = string("x_173_split_1_sigmoid_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_328)[name = string("input_409_cast_fp16")]; + tensor input_411_pad_0 = const()[name = string("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_411_mode_0 = const()[name = string("input_411_mode_0"), val = string("constant")]; + fp16 const_87_to_fp16 = const()[name = string("const_87_to_fp16"), val = fp16(0x0p+0)]; + tensor input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = string("input_411_cast_fp16")]; + string input_413_pad_type_0 = const()[name = string("input_413_pad_type_0"), val = string("valid")]; + int32 input_413_groups_0 = const()[name = string("input_413_groups_0"), val = int32(1024)]; + tensor input_413_strides_0 = const()[name = string("input_413_strides_0"), val = tensor([1])]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0])]; + tensor input_413_dilations_0 = const()[name = string("input_413_dilations_0"), val = tensor([1])]; + tensor const_262_to_fp16 = const()[name = string("const_262_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135056512)))]; + tensor const_263_to_fp16 = const()[name = string("const_263_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135075008)))]; + tensor input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16, x = input_411_cast_fp16)[name = string("input_415_cast_fp16")]; + tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = string("input_417_cast_fp16")]; + string x_175_pad_type_0 = const()[name = string("x_175_pad_type_0"), val = string("valid")]; + tensor x_175_strides_0 = const()[name = string("x_175_strides_0"), val = tensor([1])]; + tensor x_175_pad_0 = const()[name = string("x_175_pad_0"), val = tensor([0, 0])]; + tensor x_175_dilations_0 = const()[name = string("x_175_dilations_0"), val = tensor([1])]; + int32 x_175_groups_0 = const()[name = string("x_175_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135077120))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135208256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135760576))))[name = string("module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_417_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = string("transpose_257")]; + tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = string("input_421_cast_fp16")]; + tensor input_423_axes_0 = const()[name = string("input_423_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135777024)))]; + tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135779136)))]; + tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135781248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136305600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138512896))))[name = string("module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_423_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138578496))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139102848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141244032))))[name = string("module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_427_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1508_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1508_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor input_435_axes_0 = const()[name = string("input_435_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = string("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141260480)))]; + tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = string("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141262592)))]; + tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = string("input_435_cast_fp16")]; + tensor input_437_axes_0 = const()[name = string("input_437_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141264704)))]; + tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141266816)))]; + tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = string("input_437_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141268928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141793280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143997568))))[name = string("module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_437_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144063168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144587520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146688320))))[name = string("module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_441_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_1536_to_fp16 = const()[name = string("op_1536_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1537_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1536_to_fp16)[name = string("op_1537_cast_fp16")]; + tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1537_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146704768)))]; + tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146706880)))]; + tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = string("query_17_cast_fp16")]; + tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146708992))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146840128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147397184))))[name = string("module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_1553 = const()[name = string("op_1553"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_1553, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147413632))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147544768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148108864))))[name = string("module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_1557 = const()[name = string("op_1557"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_1557, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148125312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148256448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148814976))))[name = string("module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_1561 = const()[name = string("op_1561"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_1561, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148831424)))]; + tensor var_1573_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1573_cast_fp16")]; + tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148833536)))]; + tensor var_1575_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1575_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_183_transpose_x_0 = const()[name = string("x_183_transpose_x_0"), val = bool(false)]; + bool x_183_transpose_y_0 = const()[name = string("x_183_transpose_y_0"), val = bool(false)]; + tensor var_1577_to_fp16 = const()[name = string("op_1577_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148835648)))]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1575_cast_fp16)[name = string("transpose_256")]; + tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1577_to_fp16)[name = string("x_183_cast_fp16")]; + tensor x_185_pad_0 = const()[name = string("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_185_mode_0 = const()[name = string("x_185_mode_0"), val = string("constant")]; + fp16 const_94_to_fp16 = const()[name = string("const_94_to_fp16"), val = fp16(0x0p+0)]; + tensor x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor var_1585 = const()[name = string("op_1585"), val = tensor([1, 8, -1, 188])]; + tensor x_187_cast_fp16 = reshape(shape = var_1585, x = x_185_cast_fp16)[name = string("x_187_cast_fp16")]; + tensor var_1589_begin_0 = const()[name = string("op_1589_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1589_end_0 = const()[name = string("op_1589_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1589_end_mask_0 = const()[name = string("op_1589_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1589_cast_fp16 = slice_by_index(begin = var_1589_begin_0, end = var_1589_end_0, end_mask = var_1589_end_mask_0, x = x_187_cast_fp16)[name = string("op_1589_cast_fp16")]; + tensor var_1590 = const()[name = string("op_1590"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1590, x = var_1589_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1573_cast_fp16)[name = string("transpose_255")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_1599_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1599_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_1599_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = string("scores_35_cast_fp16")]; + tensor var_1605_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = string("op_1605_cast_fp16")]; + tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1605_cast_fp16, cond = mask_3)[name = string("input_449_cast_fp16")]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = string("x_189_cast_fp16")]; + tensor var_1609_perm_0 = const()[name = string("op_1609_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1610 = const()[name = string("op_1610"), val = tensor([1, -1, 1024])]; + tensor var_1609_cast_fp16 = transpose(perm = var_1609_perm_0, x = x_189_cast_fp16)[name = string("transpose_252")]; + tensor input_451_cast_fp16 = reshape(shape = var_1610, x = var_1609_cast_fp16)[name = string("input_451_cast_fp16")]; + tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149603712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149734848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150298304))))[name = string("module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_451_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = string("input_455_cast_fp16")]; + tensor x_193_axes_0 = const()[name = string("x_193_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150314752)))]; + tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150316864)))]; + tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor input_457_perm_0 = const()[name = string("input_457_perm_0"), val = tensor([0, 2, 1])]; + string input_459_pad_type_0 = const()[name = string("input_459_pad_type_0"), val = string("valid")]; + tensor input_459_strides_0 = const()[name = string("input_459_strides_0"), val = tensor([1])]; + tensor input_459_pad_0 = const()[name = string("input_459_pad_0"), val = tensor([0, 0])]; + tensor input_459_dilations_0 = const()[name = string("input_459_dilations_0"), val = tensor([1])]; + int32 input_459_groups_0 = const()[name = string("input_459_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150318976))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150581184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151707264))))[name = string("module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = string("transpose_251")]; + tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_457_cast_fp16)[name = string("input_459_cast_fp16")]; + int32 x_195_split_num_splits_0 = const()[name = string("x_195_split_num_splits_0"), val = int32(2)]; + int32 x_195_split_axis_0 = const()[name = string("x_195_split_axis_0"), val = int32(1)]; + tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = string("x_195_split_cast_fp16")]; + tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = string("x_195_split_1_sigmoid_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_328)[name = string("input_461_cast_fp16")]; + tensor input_463_pad_0 = const()[name = string("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_463_mode_0 = const()[name = string("input_463_mode_0"), val = string("constant")]; + fp16 const_97_to_fp16 = const()[name = string("const_97_to_fp16"), val = fp16(0x0p+0)]; + tensor input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = string("input_463_cast_fp16")]; + string input_465_pad_type_0 = const()[name = string("input_465_pad_type_0"), val = string("valid")]; + int32 input_465_groups_0 = const()[name = string("input_465_groups_0"), val = int32(1024)]; + tensor input_465_strides_0 = const()[name = string("input_465_strides_0"), val = tensor([1])]; + tensor input_465_pad_0 = const()[name = string("input_465_pad_0"), val = tensor([0, 0])]; + tensor input_465_dilations_0 = const()[name = string("input_465_dilations_0"), val = tensor([1])]; + tensor const_264_to_fp16 = const()[name = string("const_264_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151740096)))]; + tensor const_265_to_fp16 = const()[name = string("const_265_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151758592)))]; + tensor input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16, x = input_463_cast_fp16)[name = string("input_467_cast_fp16")]; + tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = string("input_469_cast_fp16")]; + string x_197_pad_type_0 = const()[name = string("x_197_pad_type_0"), val = string("valid")]; + tensor x_197_strides_0 = const()[name = string("x_197_strides_0"), val = tensor([1])]; + tensor x_197_pad_0 = const()[name = string("x_197_pad_0"), val = tensor([0, 0])]; + tensor x_197_dilations_0 = const()[name = string("x_197_dilations_0"), val = tensor([1])]; + int32 x_197_groups_0 = const()[name = string("x_197_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151760704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151891840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152446400))))[name = string("module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_469_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = string("transpose_250")]; + tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = string("input_473_cast_fp16")]; + tensor input_475_axes_0 = const()[name = string("input_475_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152462848)))]; + tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152464960)))]; + tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152467072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152991424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155200320))))[name = string("module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_475_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155265920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155790272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157965312))))[name = string("module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_479_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_1670_to_fp16 = const()[name = string("op_1670_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1671_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1670_to_fp16)[name = string("op_1671_cast_fp16")]; + tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1671_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor input_487_axes_0 = const()[name = string("input_487_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = string("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157981760)))]; + tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = string("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157983872)))]; + tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = string("input_487_cast_fp16")]; + tensor input_489_axes_0 = const()[name = string("input_489_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157985984)))]; + tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157988096)))]; + tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = string("input_489_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157990208))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158514560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160703936))))[name = string("module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_489_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160769536))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161293888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163392128))))[name = string("module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_493_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_1699_to_fp16 = const()[name = string("op_1699_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1700_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1699_to_fp16)[name = string("op_1700_cast_fp16")]; + tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1700_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163408576)))]; + tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163410688)))]; + tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = string("query_19_cast_fp16")]; + tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163412800))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163543936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164100672))))[name = string("module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_1716 = const()[name = string("op_1716"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_1716, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164117120))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164248256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164812672))))[name = string("module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_1720 = const()[name = string("op_1720"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_1720, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164829120))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164960256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165520832))))[name = string("module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_1724 = const()[name = string("op_1724"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_1724, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165537280)))]; + tensor var_1736_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_1736_cast_fp16")]; + tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165539392)))]; + tensor var_1738_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_1738_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_205_transpose_x_0 = const()[name = string("x_205_transpose_x_0"), val = bool(false)]; + bool x_205_transpose_y_0 = const()[name = string("x_205_transpose_y_0"), val = bool(false)]; + tensor var_1740_to_fp16 = const()[name = string("op_1740_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165541504)))]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1738_cast_fp16)[name = string("transpose_249")]; + tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1740_to_fp16)[name = string("x_205_cast_fp16")]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_207_mode_0 = const()[name = string("x_207_mode_0"), val = string("constant")]; + fp16 const_104_to_fp16 = const()[name = string("const_104_to_fp16"), val = fp16(0x0p+0)]; + tensor x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor var_1748 = const()[name = string("op_1748"), val = tensor([1, 8, -1, 188])]; + tensor x_209_cast_fp16 = reshape(shape = var_1748, x = x_207_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor var_1752_begin_0 = const()[name = string("op_1752_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1752_end_0 = const()[name = string("op_1752_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1752_end_mask_0 = const()[name = string("op_1752_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1752_cast_fp16 = slice_by_index(begin = var_1752_begin_0, end = var_1752_end_0, end_mask = var_1752_end_mask_0, x = x_209_cast_fp16)[name = string("op_1752_cast_fp16")]; + tensor var_1753 = const()[name = string("op_1753"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1753, x = var_1752_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1736_cast_fp16)[name = string("transpose_248")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_1762_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_1762_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_1762_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = string("scores_39_cast_fp16")]; + tensor var_1768_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = string("op_1768_cast_fp16")]; + tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1768_cast_fp16, cond = mask_3)[name = string("input_501_cast_fp16")]; + bool x_211_transpose_x_0 = const()[name = string("x_211_transpose_x_0"), val = bool(false)]; + bool x_211_transpose_y_0 = const()[name = string("x_211_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")]; + tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_1772_perm_0 = const()[name = string("op_1772_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1773 = const()[name = string("op_1773"), val = tensor([1, -1, 1024])]; + tensor var_1772_cast_fp16 = transpose(perm = var_1772_perm_0, x = x_211_cast_fp16)[name = string("transpose_245")]; + tensor input_503_cast_fp16 = reshape(shape = var_1773, x = var_1772_cast_fp16)[name = string("input_503_cast_fp16")]; + tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166309568))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166440704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167001536))))[name = string("module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_503_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = string("input_507_cast_fp16")]; + tensor x_215_axes_0 = const()[name = string("x_215_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167017984)))]; + tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167020096)))]; + tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor input_509_perm_0 = const()[name = string("input_509_perm_0"), val = tensor([0, 2, 1])]; + string input_511_pad_type_0 = const()[name = string("input_511_pad_type_0"), val = string("valid")]; + tensor input_511_strides_0 = const()[name = string("input_511_strides_0"), val = tensor([1])]; + tensor input_511_pad_0 = const()[name = string("input_511_pad_0"), val = tensor([0, 0])]; + tensor input_511_dilations_0 = const()[name = string("input_511_dilations_0"), val = tensor([1])]; + int32 input_511_groups_0 = const()[name = string("input_511_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167022208))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167284416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168408960))))[name = string("module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = string("transpose_244")]; + tensor input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_509_cast_fp16)[name = string("input_511_cast_fp16")]; + int32 x_217_split_num_splits_0 = const()[name = string("x_217_split_num_splits_0"), val = int32(2)]; + int32 x_217_split_axis_0 = const()[name = string("x_217_split_axis_0"), val = int32(1)]; + tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = string("x_217_split_cast_fp16")]; + tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = string("x_217_split_1_sigmoid_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_328)[name = string("input_513_cast_fp16")]; + tensor input_515_pad_0 = const()[name = string("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_515_mode_0 = const()[name = string("input_515_mode_0"), val = string("constant")]; + fp16 const_107_to_fp16 = const()[name = string("const_107_to_fp16"), val = fp16(0x0p+0)]; + tensor input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = string("input_515_cast_fp16")]; + string input_517_pad_type_0 = const()[name = string("input_517_pad_type_0"), val = string("valid")]; + int32 input_517_groups_0 = const()[name = string("input_517_groups_0"), val = int32(1024)]; + tensor input_517_strides_0 = const()[name = string("input_517_strides_0"), val = tensor([1])]; + tensor input_517_pad_0 = const()[name = string("input_517_pad_0"), val = tensor([0, 0])]; + tensor input_517_dilations_0 = const()[name = string("input_517_dilations_0"), val = tensor([1])]; + tensor const_266_to_fp16 = const()[name = string("const_266_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168441792)))]; + tensor const_267_to_fp16 = const()[name = string("const_267_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168460288)))]; + tensor input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16, x = input_515_cast_fp16)[name = string("input_519_cast_fp16")]; + tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = string("input_521_cast_fp16")]; + string x_219_pad_type_0 = const()[name = string("x_219_pad_type_0"), val = string("valid")]; + tensor x_219_strides_0 = const()[name = string("x_219_strides_0"), val = tensor([1])]; + tensor x_219_pad_0 = const()[name = string("x_219_pad_0"), val = tensor([0, 0])]; + tensor x_219_dilations_0 = const()[name = string("x_219_dilations_0"), val = tensor([1])]; + int32 x_219_groups_0 = const()[name = string("x_219_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168462400))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168593536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169142592))))[name = string("module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_521_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = string("transpose_243")]; + tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = string("input_525_cast_fp16")]; + tensor input_527_axes_0 = const()[name = string("input_527_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169159040)))]; + tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169161152)))]; + tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169163264))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169687616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171886592))))[name = string("module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_527_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171952192))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172476544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174671104))))[name = string("module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_531_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_1833_to_fp16 = const()[name = string("op_1833_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1834_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1833_to_fp16)[name = string("op_1834_cast_fp16")]; + tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1834_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor input_539_axes_0 = const()[name = string("input_539_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = string("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174687552)))]; + tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = string("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174689664)))]; + tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = string("input_539_cast_fp16")]; + tensor input_541_axes_0 = const()[name = string("input_541_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174691776)))]; + tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174693888)))]; + tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = string("input_541_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174696000))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175220352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177378560))))[name = string("module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_541_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177444160))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177968512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180085824))))[name = string("module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_545_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_1862_to_fp16 = const()[name = string("op_1862_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1863_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1862_to_fp16)[name = string("op_1863_cast_fp16")]; + tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1863_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180102272)))]; + tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180104384)))]; + tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = string("query_21_cast_fp16")]; + tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180106496))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180237632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180797056))))[name = string("module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_1879 = const()[name = string("op_1879"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_1879, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180813504))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180944640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181507008))))[name = string("module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_1883 = const()[name = string("op_1883"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_1883, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181523456))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181654592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182214080))))[name = string("module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_1887 = const()[name = string("op_1887"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_1887, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182230528)))]; + tensor var_1899_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_1899_cast_fp16")]; + tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182232640)))]; + tensor var_1901_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_1901_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_227_transpose_x_0 = const()[name = string("x_227_transpose_x_0"), val = bool(false)]; + bool x_227_transpose_y_0 = const()[name = string("x_227_transpose_y_0"), val = bool(false)]; + tensor var_1903_to_fp16 = const()[name = string("op_1903_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182234752)))]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1901_cast_fp16)[name = string("transpose_242")]; + tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_1903_to_fp16)[name = string("x_227_cast_fp16")]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_229_mode_0 = const()[name = string("x_229_mode_0"), val = string("constant")]; + fp16 const_114_to_fp16 = const()[name = string("const_114_to_fp16"), val = fp16(0x0p+0)]; + tensor x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor var_1911 = const()[name = string("op_1911"), val = tensor([1, 8, -1, 188])]; + tensor x_231_cast_fp16 = reshape(shape = var_1911, x = x_229_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor var_1915_begin_0 = const()[name = string("op_1915_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1915_end_0 = const()[name = string("op_1915_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1915_end_mask_0 = const()[name = string("op_1915_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1915_cast_fp16 = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = x_231_cast_fp16)[name = string("op_1915_cast_fp16")]; + tensor var_1916 = const()[name = string("op_1916"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_1916, x = var_1915_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1899_cast_fp16)[name = string("transpose_241")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_1925_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_1925_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_1925_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = string("scores_43_cast_fp16")]; + tensor var_1931_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = string("op_1931_cast_fp16")]; + tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_1931_cast_fp16, cond = mask_3)[name = string("input_553_cast_fp16")]; + bool x_233_transpose_x_0 = const()[name = string("x_233_transpose_x_0"), val = bool(false)]; + bool x_233_transpose_y_0 = const()[name = string("x_233_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")]; + tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor var_1935_perm_0 = const()[name = string("op_1935_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1936 = const()[name = string("op_1936"), val = tensor([1, -1, 1024])]; + tensor var_1935_cast_fp16 = transpose(perm = var_1935_perm_0, x = x_233_cast_fp16)[name = string("transpose_238")]; + tensor input_555_cast_fp16 = reshape(shape = var_1936, x = var_1935_cast_fp16)[name = string("input_555_cast_fp16")]; + tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183002816))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183133952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183697408))))[name = string("module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_555_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = string("input_559_cast_fp16")]; + tensor x_237_axes_0 = const()[name = string("x_237_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183713856)))]; + tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183715968)))]; + tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = string("x_237_cast_fp16")]; + tensor input_561_perm_0 = const()[name = string("input_561_perm_0"), val = tensor([0, 2, 1])]; + string input_563_pad_type_0 = const()[name = string("input_563_pad_type_0"), val = string("valid")]; + tensor input_563_strides_0 = const()[name = string("input_563_strides_0"), val = tensor([1])]; + tensor input_563_pad_0 = const()[name = string("input_563_pad_0"), val = tensor([0, 0])]; + tensor input_563_dilations_0 = const()[name = string("input_563_dilations_0"), val = tensor([1])]; + int32 input_563_groups_0 = const()[name = string("input_563_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183718080))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183980288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185101952))))[name = string("module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = string("transpose_237")]; + tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_561_cast_fp16)[name = string("input_563_cast_fp16")]; + int32 x_239_split_num_splits_0 = const()[name = string("x_239_split_num_splits_0"), val = int32(2)]; + int32 x_239_split_axis_0 = const()[name = string("x_239_split_axis_0"), val = int32(1)]; + tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = string("x_239_split_cast_fp16")]; + tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = string("x_239_split_1_sigmoid_cast_fp16")]; + tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = string("x_239_cast_fp16")]; + tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_328)[name = string("input_565_cast_fp16")]; + tensor input_567_pad_0 = const()[name = string("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_567_mode_0 = const()[name = string("input_567_mode_0"), val = string("constant")]; + fp16 const_117_to_fp16 = const()[name = string("const_117_to_fp16"), val = fp16(0x0p+0)]; + tensor input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = string("input_567_cast_fp16")]; + string input_569_pad_type_0 = const()[name = string("input_569_pad_type_0"), val = string("valid")]; + int32 input_569_groups_0 = const()[name = string("input_569_groups_0"), val = int32(1024)]; + tensor input_569_strides_0 = const()[name = string("input_569_strides_0"), val = tensor([1])]; + tensor input_569_pad_0 = const()[name = string("input_569_pad_0"), val = tensor([0, 0])]; + tensor input_569_dilations_0 = const()[name = string("input_569_dilations_0"), val = tensor([1])]; + tensor const_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185134784)))]; + tensor const_269_to_fp16 = const()[name = string("const_269_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185153280)))]; + tensor input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16, x = input_567_cast_fp16)[name = string("input_571_cast_fp16")]; + tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = string("input_573_cast_fp16")]; + string x_241_pad_type_0 = const()[name = string("x_241_pad_type_0"), val = string("valid")]; + tensor x_241_strides_0 = const()[name = string("x_241_strides_0"), val = tensor([1])]; + tensor x_241_pad_0 = const()[name = string("x_241_pad_0"), val = tensor([0, 0])]; + tensor x_241_dilations_0 = const()[name = string("x_241_dilations_0"), val = tensor([1])]; + int32 x_241_groups_0 = const()[name = string("x_241_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185155392))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185286528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185838784))))[name = string("module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_573_cast_fp16)[name = string("x_241_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = string("transpose_236")]; + tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = string("input_577_cast_fp16")]; + tensor input_579_axes_0 = const()[name = string("input_579_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185855232)))]; + tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185857344)))]; + tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185859456))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186383808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188571712))))[name = string("module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_579_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188637312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189161664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191330432))))[name = string("module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_583_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_1996_to_fp16 = const()[name = string("op_1996_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1997_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1996_to_fp16)[name = string("op_1997_cast_fp16")]; + tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1997_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor input_591_axes_0 = const()[name = string("input_591_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = string("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191346880)))]; + tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = string("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191348992)))]; + tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = string("input_591_cast_fp16")]; + tensor input_593_axes_0 = const()[name = string("input_593_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191351104)))]; + tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191353216)))]; + tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = string("input_593_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191355328))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191879680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194036352))))[name = string("module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_593_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194101952))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194626304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196774656))))[name = string("module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_597_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2025_to_fp16 = const()[name = string("op_2025_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2026_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2025_to_fp16)[name = string("op_2026_cast_fp16")]; + tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2026_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196791104)))]; + tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196793216)))]; + tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = string("query_23_cast_fp16")]; + tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196795328))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196926464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197485504))))[name = string("module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2042 = const()[name = string("op_2042"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2042, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197501952))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197633088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198196864))))[name = string("module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2046 = const()[name = string("op_2046"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2046, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198213312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198344448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198903680))))[name = string("module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2050 = const()[name = string("op_2050"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2050, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198920128)))]; + tensor var_2062_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2062_cast_fp16")]; + tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198922240)))]; + tensor var_2064_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2064_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_249_transpose_x_0 = const()[name = string("x_249_transpose_x_0"), val = bool(false)]; + bool x_249_transpose_y_0 = const()[name = string("x_249_transpose_y_0"), val = bool(false)]; + tensor var_2066_to_fp16 = const()[name = string("op_2066_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198924352)))]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2064_cast_fp16)[name = string("transpose_235")]; + tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2066_to_fp16)[name = string("x_249_cast_fp16")]; + tensor x_251_pad_0 = const()[name = string("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_251_mode_0 = const()[name = string("x_251_mode_0"), val = string("constant")]; + fp16 const_124_to_fp16 = const()[name = string("const_124_to_fp16"), val = fp16(0x0p+0)]; + tensor x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor var_2074 = const()[name = string("op_2074"), val = tensor([1, 8, -1, 188])]; + tensor x_253_cast_fp16 = reshape(shape = var_2074, x = x_251_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor var_2078_begin_0 = const()[name = string("op_2078_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2078_end_0 = const()[name = string("op_2078_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2078_end_mask_0 = const()[name = string("op_2078_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = x_253_cast_fp16)[name = string("op_2078_cast_fp16")]; + tensor var_2079 = const()[name = string("op_2079"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2079, x = var_2078_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2062_cast_fp16)[name = string("transpose_234")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2088_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2088_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2088_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = string("scores_47_cast_fp16")]; + tensor var_2094_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2094_cast_fp16, cond = mask_3)[name = string("input_605_cast_fp16")]; + bool x_255_transpose_x_0 = const()[name = string("x_255_transpose_x_0"), val = bool(false)]; + bool x_255_transpose_y_0 = const()[name = string("x_255_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")]; + tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2099 = const()[name = string("op_2099"), val = tensor([1, -1, 1024])]; + tensor var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = x_255_cast_fp16)[name = string("transpose_231")]; + tensor input_607_cast_fp16 = reshape(shape = var_2099, x = var_2098_cast_fp16)[name = string("input_607_cast_fp16")]; + tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199692416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199823552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200388288))))[name = string("module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_607_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = string("input_611_cast_fp16")]; + tensor x_259_axes_0 = const()[name = string("x_259_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200404736)))]; + tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200406848)))]; + tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_613_perm_0 = const()[name = string("input_613_perm_0"), val = tensor([0, 2, 1])]; + string input_615_pad_type_0 = const()[name = string("input_615_pad_type_0"), val = string("valid")]; + tensor input_615_strides_0 = const()[name = string("input_615_strides_0"), val = tensor([1])]; + tensor input_615_pad_0 = const()[name = string("input_615_pad_0"), val = tensor([0, 0])]; + tensor input_615_dilations_0 = const()[name = string("input_615_dilations_0"), val = tensor([1])]; + int32 input_615_groups_0 = const()[name = string("input_615_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200408960))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200671168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201789760))))[name = string("module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = string("transpose_230")]; + tensor input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_613_cast_fp16)[name = string("input_615_cast_fp16")]; + int32 x_261_split_num_splits_0 = const()[name = string("x_261_split_num_splits_0"), val = int32(2)]; + int32 x_261_split_axis_0 = const()[name = string("x_261_split_axis_0"), val = int32(1)]; + tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = string("x_261_split_cast_fp16")]; + tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = string("x_261_split_1_sigmoid_cast_fp16")]; + tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = string("x_261_cast_fp16")]; + tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_328)[name = string("input_617_cast_fp16")]; + tensor input_619_pad_0 = const()[name = string("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_619_mode_0 = const()[name = string("input_619_mode_0"), val = string("constant")]; + fp16 const_127_to_fp16 = const()[name = string("const_127_to_fp16"), val = fp16(0x0p+0)]; + tensor input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = string("input_619_cast_fp16")]; + string input_621_pad_type_0 = const()[name = string("input_621_pad_type_0"), val = string("valid")]; + int32 input_621_groups_0 = const()[name = string("input_621_groups_0"), val = int32(1024)]; + tensor input_621_strides_0 = const()[name = string("input_621_strides_0"), val = tensor([1])]; + tensor input_621_pad_0 = const()[name = string("input_621_pad_0"), val = tensor([0, 0])]; + tensor input_621_dilations_0 = const()[name = string("input_621_dilations_0"), val = tensor([1])]; + tensor const_270_to_fp16 = const()[name = string("const_270_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201822592)))]; + tensor const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201841088)))]; + tensor input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16, x = input_619_cast_fp16)[name = string("input_623_cast_fp16")]; + tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = string("input_625_cast_fp16")]; + string x_263_pad_type_0 = const()[name = string("x_263_pad_type_0"), val = string("valid")]; + tensor x_263_strides_0 = const()[name = string("x_263_strides_0"), val = tensor([1])]; + tensor x_263_pad_0 = const()[name = string("x_263_pad_0"), val = tensor([0, 0])]; + tensor x_263_dilations_0 = const()[name = string("x_263_dilations_0"), val = tensor([1])]; + int32 x_263_groups_0 = const()[name = string("x_263_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201843200))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201974336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202528448))))[name = string("module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_625_cast_fp16)[name = string("x_263_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = string("transpose_229")]; + tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = string("input_629_cast_fp16")]; + tensor input_631_axes_0 = const()[name = string("input_631_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202544896)))]; + tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202547008)))]; + tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202549120))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203073472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205269056))))[name = string("module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_631_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205334656))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205859008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207977536))))[name = string("module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_635_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2160_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2159_to_fp16)[name = string("op_2160_cast_fp16")]; + tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2160_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor input_643_axes_0 = const()[name = string("input_643_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = string("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207993984)))]; + tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = string("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207996096)))]; + tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = string("input_643_cast_fp16")]; + tensor input_645_axes_0 = const()[name = string("input_645_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207998208)))]; + tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208000320)))]; + tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = string("input_645_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208002432))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208526784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210684288))))[name = string("module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_645_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210749888))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211274240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213392256))))[name = string("module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_649_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_2188_to_fp16 = const()[name = string("op_2188_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2189_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2188_to_fp16)[name = string("op_2189_cast_fp16")]; + tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2189_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213408704)))]; + tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213410816)))]; + tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = string("query_25_cast_fp16")]; + tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213412928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213544064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214102528))))[name = string("module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_2205 = const()[name = string("op_2205"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_2205, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214118976))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214250112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214812416))))[name = string("module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_2209 = const()[name = string("op_2209"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_2209, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214828864))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214960000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215518144))))[name = string("module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_2213 = const()[name = string("op_2213"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_2213, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215534592)))]; + tensor var_2225_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2225_cast_fp16")]; + tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215536704)))]; + tensor var_2227_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2227_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_271_transpose_x_0 = const()[name = string("x_271_transpose_x_0"), val = bool(false)]; + bool x_271_transpose_y_0 = const()[name = string("x_271_transpose_y_0"), val = bool(false)]; + tensor var_2229_to_fp16 = const()[name = string("op_2229_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215538816)))]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2227_cast_fp16)[name = string("transpose_228")]; + tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2229_to_fp16)[name = string("x_271_cast_fp16")]; + tensor x_273_pad_0 = const()[name = string("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_273_mode_0 = const()[name = string("x_273_mode_0"), val = string("constant")]; + fp16 const_134_to_fp16 = const()[name = string("const_134_to_fp16"), val = fp16(0x0p+0)]; + tensor x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2237 = const()[name = string("op_2237"), val = tensor([1, 8, -1, 188])]; + tensor x_275_cast_fp16 = reshape(shape = var_2237, x = x_273_cast_fp16)[name = string("x_275_cast_fp16")]; + tensor var_2241_begin_0 = const()[name = string("op_2241_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2241_end_0 = const()[name = string("op_2241_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2241_end_mask_0 = const()[name = string("op_2241_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2241_cast_fp16 = slice_by_index(begin = var_2241_begin_0, end = var_2241_end_0, end_mask = var_2241_end_mask_0, x = x_275_cast_fp16)[name = string("op_2241_cast_fp16")]; + tensor var_2242 = const()[name = string("op_2242"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2242, x = var_2241_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2225_cast_fp16)[name = string("transpose_227")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_2251_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2251_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_2251_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = string("scores_51_cast_fp16")]; + tensor var_2257_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = string("op_2257_cast_fp16")]; + tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2257_cast_fp16, cond = mask_3)[name = string("input_657_cast_fp16")]; + bool x_277_transpose_x_0 = const()[name = string("x_277_transpose_x_0"), val = bool(false)]; + bool x_277_transpose_y_0 = const()[name = string("x_277_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")]; + tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor var_2261_perm_0 = const()[name = string("op_2261_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2262 = const()[name = string("op_2262"), val = tensor([1, -1, 1024])]; + tensor var_2261_cast_fp16 = transpose(perm = var_2261_perm_0, x = x_277_cast_fp16)[name = string("transpose_224")]; + tensor input_659_cast_fp16 = reshape(shape = var_2262, x = var_2261_cast_fp16)[name = string("input_659_cast_fp16")]; + tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216306880))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216438016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217005248))))[name = string("module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_659_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = string("input_663_cast_fp16")]; + tensor x_281_axes_0 = const()[name = string("x_281_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217021696)))]; + tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217023808)))]; + tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_665_perm_0 = const()[name = string("input_665_perm_0"), val = tensor([0, 2, 1])]; + string input_667_pad_type_0 = const()[name = string("input_667_pad_type_0"), val = string("valid")]; + tensor input_667_strides_0 = const()[name = string("input_667_strides_0"), val = tensor([1])]; + tensor input_667_pad_0 = const()[name = string("input_667_pad_0"), val = tensor([0, 0])]; + tensor input_667_dilations_0 = const()[name = string("input_667_dilations_0"), val = tensor([1])]; + int32 input_667_groups_0 = const()[name = string("input_667_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217025920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217288128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218402752))))[name = string("module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = string("transpose_223")]; + tensor input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_665_cast_fp16)[name = string("input_667_cast_fp16")]; + int32 x_283_split_num_splits_0 = const()[name = string("x_283_split_num_splits_0"), val = int32(2)]; + int32 x_283_split_axis_0 = const()[name = string("x_283_split_axis_0"), val = int32(1)]; + tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = string("x_283_split_cast_fp16")]; + tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = string("x_283_split_1_sigmoid_cast_fp16")]; + tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_328)[name = string("input_669_cast_fp16")]; + tensor input_671_pad_0 = const()[name = string("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_671_mode_0 = const()[name = string("input_671_mode_0"), val = string("constant")]; + fp16 const_137_to_fp16 = const()[name = string("const_137_to_fp16"), val = fp16(0x0p+0)]; + tensor input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = string("input_671_cast_fp16")]; + string input_673_pad_type_0 = const()[name = string("input_673_pad_type_0"), val = string("valid")]; + int32 input_673_groups_0 = const()[name = string("input_673_groups_0"), val = int32(1024)]; + tensor input_673_strides_0 = const()[name = string("input_673_strides_0"), val = tensor([1])]; + tensor input_673_pad_0 = const()[name = string("input_673_pad_0"), val = tensor([0, 0])]; + tensor input_673_dilations_0 = const()[name = string("input_673_dilations_0"), val = tensor([1])]; + tensor const_272_to_fp16 = const()[name = string("const_272_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218435584)))]; + tensor const_273_to_fp16 = const()[name = string("const_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218454080)))]; + tensor input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16, x = input_671_cast_fp16)[name = string("input_675_cast_fp16")]; + tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = string("input_677_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218456192))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218587328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219140672))))[name = string("module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_677_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = string("transpose_222")]; + tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = string("input_681_cast_fp16")]; + tensor input_683_axes_0 = const()[name = string("input_683_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219157120)))]; + tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219159232)))]; + tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219161344))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219685696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221890176))))[name = string("module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_683_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221955776))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222480128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224582592))))[name = string("module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_687_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_2322_to_fp16 = const()[name = string("op_2322_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2323_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2322_to_fp16)[name = string("op_2323_cast_fp16")]; + tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2323_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor input_695_axes_0 = const()[name = string("input_695_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = string("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224599040)))]; + tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = string("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224601152)))]; + tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = string("input_695_cast_fp16")]; + tensor input_697_axes_0 = const()[name = string("input_697_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224603264)))]; + tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224605376)))]; + tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = string("input_697_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224607488))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225131840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227299136))))[name = string("module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_697_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227364736))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227889088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229965376))))[name = string("module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_701_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_2351_to_fp16 = const()[name = string("op_2351_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2352_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2351_to_fp16)[name = string("op_2352_cast_fp16")]; + tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2352_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229981824)))]; + tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229983936)))]; + tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = string("query_27_cast_fp16")]; + tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229986048))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230117184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230675072))))[name = string("module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_2368 = const()[name = string("op_2368"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_2368, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230691520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230822656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231382144))))[name = string("module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_2372 = const()[name = string("op_2372"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_2372, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231398592))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231529728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232085696))))[name = string("module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_2376 = const()[name = string("op_2376"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_2376, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232102144)))]; + tensor var_2388_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2388_cast_fp16")]; + tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232104256)))]; + tensor var_2390_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2390_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor var_2392_to_fp16 = const()[name = string("op_2392_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232106368)))]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2390_cast_fp16)[name = string("transpose_221")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2392_to_fp16)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2400 = const()[name = string("op_2400"), val = tensor([1, 8, -1, 188])]; + tensor x_297_cast_fp16 = reshape(shape = var_2400, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2404_begin_0 = const()[name = string("op_2404_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2404_end_0 = const()[name = string("op_2404_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2404_end_mask_0 = const()[name = string("op_2404_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2404_cast_fp16 = slice_by_index(begin = var_2404_begin_0, end = var_2404_end_0, end_mask = var_2404_end_mask_0, x = x_297_cast_fp16)[name = string("op_2404_cast_fp16")]; + tensor var_2405 = const()[name = string("op_2405"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2405, x = var_2404_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2388_cast_fp16)[name = string("transpose_220")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_2414_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2414_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_2414_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = string("scores_55_cast_fp16")]; + tensor var_2420_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = string("op_2420_cast_fp16")]; + tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2420_cast_fp16, cond = mask_3)[name = string("input_709_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2424_perm_0 = const()[name = string("op_2424_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2425 = const()[name = string("op_2425"), val = tensor([1, -1, 1024])]; + tensor var_2424_cast_fp16 = transpose(perm = var_2424_perm_0, x = x_299_cast_fp16)[name = string("transpose_217")]; + tensor input_711_cast_fp16 = reshape(shape = var_2425, x = var_2424_cast_fp16)[name = string("input_711_cast_fp16")]; + tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232874432))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233005568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233571264))))[name = string("module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_711_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = string("input_715_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233587712)))]; + tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233589824)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_717_perm_0 = const()[name = string("input_717_perm_0"), val = tensor([0, 2, 1])]; + string input_719_pad_type_0 = const()[name = string("input_719_pad_type_0"), val = string("valid")]; + tensor input_719_strides_0 = const()[name = string("input_719_strides_0"), val = tensor([1])]; + tensor input_719_pad_0 = const()[name = string("input_719_pad_0"), val = tensor([0, 0])]; + tensor input_719_dilations_0 = const()[name = string("input_719_dilations_0"), val = tensor([1])]; + int32 input_719_groups_0 = const()[name = string("input_719_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233591936))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233854144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234969088))))[name = string("module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = string("transpose_216")]; + tensor input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_717_cast_fp16)[name = string("input_719_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_328)[name = string("input_721_cast_fp16")]; + tensor input_723_pad_0 = const()[name = string("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_723_mode_0 = const()[name = string("input_723_mode_0"), val = string("constant")]; + fp16 const_147_to_fp16 = const()[name = string("const_147_to_fp16"), val = fp16(0x0p+0)]; + tensor input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = string("input_723_cast_fp16")]; + string input_725_pad_type_0 = const()[name = string("input_725_pad_type_0"), val = string("valid")]; + int32 input_725_groups_0 = const()[name = string("input_725_groups_0"), val = int32(1024)]; + tensor input_725_strides_0 = const()[name = string("input_725_strides_0"), val = tensor([1])]; + tensor input_725_pad_0 = const()[name = string("input_725_pad_0"), val = tensor([0, 0])]; + tensor input_725_dilations_0 = const()[name = string("input_725_dilations_0"), val = tensor([1])]; + tensor const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235001920)))]; + tensor const_275_to_fp16 = const()[name = string("const_275_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235020416)))]; + tensor input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16, x = input_723_cast_fp16)[name = string("input_727_cast_fp16")]; + tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = string("input_729_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235022528))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235153664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235708032))))[name = string("module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_729_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = string("transpose_215")]; + tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = string("input_733_cast_fp16")]; + tensor input_735_axes_0 = const()[name = string("input_735_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235724480)))]; + tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235726592)))]; + tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235728704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236253056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238460224))))[name = string("module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_735_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238525824))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239050176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241152768))))[name = string("module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_739_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_2485_to_fp16 = const()[name = string("op_2485_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2486_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2485_to_fp16)[name = string("op_2486_cast_fp16")]; + tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2486_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor input_747_axes_0 = const()[name = string("input_747_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = string("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241169216)))]; + tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = string("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241171328)))]; + tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = string("input_747_cast_fp16")]; + tensor input_749_axes_0 = const()[name = string("input_749_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241173440)))]; + tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241175552)))]; + tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = string("input_749_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241177664))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241702016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243874880))))[name = string("module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_749_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243940480))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244464832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246509312))))[name = string("module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_753_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_2514_to_fp16 = const()[name = string("op_2514_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2515_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2514_to_fp16)[name = string("op_2515_cast_fp16")]; + tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2515_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246525760)))]; + tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246527872)))]; + tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = string("query_29_cast_fp16")]; + tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246529984))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246661120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247219264))))[name = string("module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_2531 = const()[name = string("op_2531"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_2531, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247235712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247366848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247927744))))[name = string("module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_2535 = const()[name = string("op_2535"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_2535, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247944192))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248075328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248632128))))[name = string("module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_2539 = const()[name = string("op_2539"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_2539, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248648576)))]; + tensor var_2551_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2551_cast_fp16")]; + tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248650688)))]; + tensor var_2553_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2553_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_315_transpose_x_0 = const()[name = string("x_315_transpose_x_0"), val = bool(false)]; + bool x_315_transpose_y_0 = const()[name = string("x_315_transpose_y_0"), val = bool(false)]; + tensor var_2555_to_fp16 = const()[name = string("op_2555_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248652800)))]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2553_cast_fp16)[name = string("transpose_214")]; + tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2555_to_fp16)[name = string("x_315_cast_fp16")]; + tensor x_317_pad_0 = const()[name = string("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_317_mode_0 = const()[name = string("x_317_mode_0"), val = string("constant")]; + fp16 const_154_to_fp16 = const()[name = string("const_154_to_fp16"), val = fp16(0x0p+0)]; + tensor x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")]; + tensor var_2563 = const()[name = string("op_2563"), val = tensor([1, 8, -1, 188])]; + tensor x_319_cast_fp16 = reshape(shape = var_2563, x = x_317_cast_fp16)[name = string("x_319_cast_fp16")]; + tensor var_2567_begin_0 = const()[name = string("op_2567_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2567_end_0 = const()[name = string("op_2567_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2567_end_mask_0 = const()[name = string("op_2567_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2567_cast_fp16 = slice_by_index(begin = var_2567_begin_0, end = var_2567_end_0, end_mask = var_2567_end_mask_0, x = x_319_cast_fp16)[name = string("op_2567_cast_fp16")]; + tensor var_2568 = const()[name = string("op_2568"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2568, x = var_2567_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2551_cast_fp16)[name = string("transpose_213")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_2577_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_2577_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_2577_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = string("scores_59_cast_fp16")]; + tensor var_2583_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = string("op_2583_cast_fp16")]; + tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2583_cast_fp16, cond = mask_3)[name = string("input_761_cast_fp16")]; + bool x_321_transpose_x_0 = const()[name = string("x_321_transpose_x_0"), val = bool(false)]; + bool x_321_transpose_y_0 = const()[name = string("x_321_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")]; + tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_2587_perm_0 = const()[name = string("op_2587_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2588 = const()[name = string("op_2588"), val = tensor([1, -1, 1024])]; + tensor var_2587_cast_fp16 = transpose(perm = var_2587_perm_0, x = x_321_cast_fp16)[name = string("transpose_210")]; + tensor input_763_cast_fp16 = reshape(shape = var_2588, x = var_2587_cast_fp16)[name = string("input_763_cast_fp16")]; + tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249420864))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249552000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250113984))))[name = string("module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_763_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = string("input_767_cast_fp16")]; + tensor x_325_axes_0 = const()[name = string("x_325_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250130432)))]; + tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250132544)))]; + tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor input_769_perm_0 = const()[name = string("input_769_perm_0"), val = tensor([0, 2, 1])]; + string input_771_pad_type_0 = const()[name = string("input_771_pad_type_0"), val = string("valid")]; + tensor input_771_strides_0 = const()[name = string("input_771_strides_0"), val = tensor([1])]; + tensor input_771_pad_0 = const()[name = string("input_771_pad_0"), val = tensor([0, 0])]; + tensor input_771_dilations_0 = const()[name = string("input_771_dilations_0"), val = tensor([1])]; + int32 input_771_groups_0 = const()[name = string("input_771_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250134656))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250396864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251510912))))[name = string("module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = string("transpose_209")]; + tensor input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_769_cast_fp16)[name = string("input_771_cast_fp16")]; + int32 x_327_split_num_splits_0 = const()[name = string("x_327_split_num_splits_0"), val = int32(2)]; + int32 x_327_split_axis_0 = const()[name = string("x_327_split_axis_0"), val = int32(1)]; + tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = string("x_327_split_cast_fp16")]; + tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = string("x_327_split_1_sigmoid_cast_fp16")]; + tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = string("x_327_cast_fp16")]; + tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_328)[name = string("input_773_cast_fp16")]; + tensor input_775_pad_0 = const()[name = string("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_775_mode_0 = const()[name = string("input_775_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = string("input_775_cast_fp16")]; + string input_777_pad_type_0 = const()[name = string("input_777_pad_type_0"), val = string("valid")]; + int32 input_777_groups_0 = const()[name = string("input_777_groups_0"), val = int32(1024)]; + tensor input_777_strides_0 = const()[name = string("input_777_strides_0"), val = tensor([1])]; + tensor input_777_pad_0 = const()[name = string("input_777_pad_0"), val = tensor([0, 0])]; + tensor input_777_dilations_0 = const()[name = string("input_777_dilations_0"), val = tensor([1])]; + tensor const_276_to_fp16 = const()[name = string("const_276_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251543744)))]; + tensor const_277_to_fp16 = const()[name = string("const_277_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251562240)))]; + tensor input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16, x = input_775_cast_fp16)[name = string("input_779_cast_fp16")]; + tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = string("input_781_cast_fp16")]; + string x_329_pad_type_0 = const()[name = string("x_329_pad_type_0"), val = string("valid")]; + tensor x_329_strides_0 = const()[name = string("x_329_strides_0"), val = tensor([1])]; + tensor x_329_pad_0 = const()[name = string("x_329_pad_0"), val = tensor([0, 0])]; + tensor x_329_dilations_0 = const()[name = string("x_329_dilations_0"), val = tensor([1])]; + int32 x_329_groups_0 = const()[name = string("x_329_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251564352))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251695488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252252096))))[name = string("module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_781_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = string("transpose_208")]; + tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = string("input_785_cast_fp16")]; + tensor input_787_axes_0 = const()[name = string("input_787_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252268544)))]; + tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252270656)))]; + tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252272768))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252797120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255004096))))[name = string("module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_787_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255069696))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255594048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257697472))))[name = string("module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_791_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_2648_to_fp16 = const()[name = string("op_2648_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2649_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2648_to_fp16)[name = string("op_2649_cast_fp16")]; + tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2649_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor input_799_axes_0 = const()[name = string("input_799_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = string("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257713920)))]; + tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = string("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257716032)))]; + tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = string("input_799_cast_fp16")]; + tensor input_801_axes_0 = const()[name = string("input_801_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257718144)))]; + tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257720256)))]; + tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = string("input_801_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257722368))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258246720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260431680))))[name = string("module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_801_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260497280))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261021632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263060032))))[name = string("module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_805_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_2677_to_fp16 = const()[name = string("op_2677_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2678_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2677_to_fp16)[name = string("op_2678_cast_fp16")]; + tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2678_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263076480)))]; + tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263078592)))]; + tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = string("query_31_cast_fp16")]; + tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263080704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263211840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263771072))))[name = string("module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_2694 = const()[name = string("op_2694"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_2694, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263787520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263918656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264480768))))[name = string("module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_2698 = const()[name = string("op_2698"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_2698, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264497216))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264628352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265185280))))[name = string("module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_2702 = const()[name = string("op_2702"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_2702, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265201728)))]; + tensor var_2714_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_2714_cast_fp16")]; + tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265203840)))]; + tensor var_2716_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_2716_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_337_transpose_x_0 = const()[name = string("x_337_transpose_x_0"), val = bool(false)]; + bool x_337_transpose_y_0 = const()[name = string("x_337_transpose_y_0"), val = bool(false)]; + tensor var_2718_to_fp16 = const()[name = string("op_2718_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265205952)))]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2716_cast_fp16)[name = string("transpose_207")]; + tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_2718_to_fp16)[name = string("x_337_cast_fp16")]; + tensor x_339_pad_0 = const()[name = string("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_339_mode_0 = const()[name = string("x_339_mode_0"), val = string("constant")]; + fp16 const_164_to_fp16 = const()[name = string("const_164_to_fp16"), val = fp16(0x0p+0)]; + tensor x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")]; + tensor var_2726 = const()[name = string("op_2726"), val = tensor([1, 8, -1, 188])]; + tensor x_341_cast_fp16 = reshape(shape = var_2726, x = x_339_cast_fp16)[name = string("x_341_cast_fp16")]; + tensor var_2730_begin_0 = const()[name = string("op_2730_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2730_end_0 = const()[name = string("op_2730_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2730_end_mask_0 = const()[name = string("op_2730_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2730_cast_fp16 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_341_cast_fp16)[name = string("op_2730_cast_fp16")]; + tensor var_2731 = const()[name = string("op_2731"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_2731, x = var_2730_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2714_cast_fp16)[name = string("transpose_206")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_2740_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_2740_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_2740_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = string("scores_63_cast_fp16")]; + tensor var_2746_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = string("op_2746_cast_fp16")]; + tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2746_cast_fp16, cond = mask_3)[name = string("input_813_cast_fp16")]; + bool x_343_transpose_x_0 = const()[name = string("x_343_transpose_x_0"), val = bool(false)]; + bool x_343_transpose_y_0 = const()[name = string("x_343_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")]; + tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = string("x_343_cast_fp16")]; + tensor var_2750_perm_0 = const()[name = string("op_2750_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2751 = const()[name = string("op_2751"), val = tensor([1, -1, 1024])]; + tensor var_2750_cast_fp16 = transpose(perm = var_2750_perm_0, x = x_343_cast_fp16)[name = string("transpose_203")]; + tensor input_815_cast_fp16 = reshape(shape = var_2751, x = var_2750_cast_fp16)[name = string("input_815_cast_fp16")]; + tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265974016))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266105152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266666880))))[name = string("module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_815_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = string("input_819_cast_fp16")]; + tensor x_347_axes_0 = const()[name = string("x_347_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266683328)))]; + tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266685440)))]; + tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor input_821_perm_0 = const()[name = string("input_821_perm_0"), val = tensor([0, 2, 1])]; + string input_823_pad_type_0 = const()[name = string("input_823_pad_type_0"), val = string("valid")]; + tensor input_823_strides_0 = const()[name = string("input_823_strides_0"), val = tensor([1])]; + tensor input_823_pad_0 = const()[name = string("input_823_pad_0"), val = tensor([0, 0])]; + tensor input_823_dilations_0 = const()[name = string("input_823_dilations_0"), val = tensor([1])]; + int32 input_823_groups_0 = const()[name = string("input_823_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266687552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266949760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268067136))))[name = string("module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = string("transpose_202")]; + tensor input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_821_cast_fp16)[name = string("input_823_cast_fp16")]; + int32 x_349_split_num_splits_0 = const()[name = string("x_349_split_num_splits_0"), val = int32(2)]; + int32 x_349_split_axis_0 = const()[name = string("x_349_split_axis_0"), val = int32(1)]; + tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = string("x_349_split_cast_fp16")]; + tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = string("x_349_split_1_sigmoid_cast_fp16")]; + tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_328)[name = string("input_825_cast_fp16")]; + tensor input_827_pad_0 = const()[name = string("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_827_mode_0 = const()[name = string("input_827_mode_0"), val = string("constant")]; + fp16 const_167_to_fp16 = const()[name = string("const_167_to_fp16"), val = fp16(0x0p+0)]; + tensor input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = string("input_827_cast_fp16")]; + string input_829_pad_type_0 = const()[name = string("input_829_pad_type_0"), val = string("valid")]; + int32 input_829_groups_0 = const()[name = string("input_829_groups_0"), val = int32(1024)]; + tensor input_829_strides_0 = const()[name = string("input_829_strides_0"), val = tensor([1])]; + tensor input_829_pad_0 = const()[name = string("input_829_pad_0"), val = tensor([0, 0])]; + tensor input_829_dilations_0 = const()[name = string("input_829_dilations_0"), val = tensor([1])]; + tensor const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268099968)))]; + tensor const_279_to_fp16 = const()[name = string("const_279_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268118464)))]; + tensor input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16, x = input_827_cast_fp16)[name = string("input_831_cast_fp16")]; + tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = string("input_833_cast_fp16")]; + string x_351_pad_type_0 = const()[name = string("x_351_pad_type_0"), val = string("valid")]; + tensor x_351_strides_0 = const()[name = string("x_351_strides_0"), val = tensor([1])]; + tensor x_351_pad_0 = const()[name = string("x_351_pad_0"), val = tensor([0, 0])]; + tensor x_351_dilations_0 = const()[name = string("x_351_dilations_0"), val = tensor([1])]; + int32 x_351_groups_0 = const()[name = string("x_351_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268120576))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268251712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268806464))))[name = string("module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_833_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = string("transpose_201")]; + tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = string("input_837_cast_fp16")]; + tensor input_839_axes_0 = const()[name = string("input_839_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268822912)))]; + tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268825024)))]; + tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268827136))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269351488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271562112))))[name = string("module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_839_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271627712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272152064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274220864))))[name = string("module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_843_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_2811_to_fp16 = const()[name = string("op_2811_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2812_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2811_to_fp16)[name = string("op_2812_cast_fp16")]; + tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2812_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor input_851_axes_0 = const()[name = string("input_851_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = string("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274237312)))]; + tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = string("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274239424)))]; + tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = string("input_851_cast_fp16")]; + tensor input_853_axes_0 = const()[name = string("input_853_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274241536)))]; + tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274243648)))]; + tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = string("input_853_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274245760))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274770112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276964544))))[name = string("module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_853_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277030144))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277554496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279597504))))[name = string("module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_857_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_2840_to_fp16 = const()[name = string("op_2840_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2841_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2840_to_fp16)[name = string("op_2841_cast_fp16")]; + tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2841_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279613952)))]; + tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616064)))]; + tensor query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = string("query_33_cast_fp16")]; + tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279618176))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279749312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280307200))))[name = string("module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_2857 = const()[name = string("op_2857"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_2857, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280323648))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280454784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281018752))))[name = string("module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_2861 = const()[name = string("op_2861"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_2861, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281035200))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281166336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281725824))))[name = string("module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_2865 = const()[name = string("op_2865"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_2865, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281742272)))]; + tensor var_2877_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_2877_cast_fp16")]; + tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281744384)))]; + tensor var_2879_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_2879_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_359_transpose_x_0 = const()[name = string("x_359_transpose_x_0"), val = bool(false)]; + bool x_359_transpose_y_0 = const()[name = string("x_359_transpose_y_0"), val = bool(false)]; + tensor var_2881_to_fp16 = const()[name = string("op_2881_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281746496)))]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2879_cast_fp16)[name = string("transpose_200")]; + tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = var_2881_to_fp16)[name = string("x_359_cast_fp16")]; + tensor x_361_pad_0 = const()[name = string("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_361_mode_0 = const()[name = string("x_361_mode_0"), val = string("constant")]; + fp16 const_174_to_fp16 = const()[name = string("const_174_to_fp16"), val = fp16(0x0p+0)]; + tensor x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor var_2889 = const()[name = string("op_2889"), val = tensor([1, 8, -1, 188])]; + tensor x_363_cast_fp16 = reshape(shape = var_2889, x = x_361_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor var_2893_begin_0 = const()[name = string("op_2893_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2893_end_0 = const()[name = string("op_2893_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2893_end_mask_0 = const()[name = string("op_2893_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2893_cast_fp16 = slice_by_index(begin = var_2893_begin_0, end = var_2893_end_0, end_mask = var_2893_end_mask_0, x = x_363_cast_fp16)[name = string("op_2893_cast_fp16")]; + tensor var_2894 = const()[name = string("op_2894"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_2894, x = var_2893_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2877_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_2903_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_2903_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_2903_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = string("scores_67_cast_fp16")]; + tensor var_2909_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = string("op_2909_cast_fp16")]; + tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_2909_cast_fp16, cond = mask_3)[name = string("input_865_cast_fp16")]; + bool x_365_transpose_x_0 = const()[name = string("x_365_transpose_x_0"), val = bool(false)]; + bool x_365_transpose_y_0 = const()[name = string("x_365_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")]; + tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_35_cast_fp16)[name = string("x_365_cast_fp16")]; + tensor var_2913_perm_0 = const()[name = string("op_2913_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2914 = const()[name = string("op_2914"), val = tensor([1, -1, 1024])]; + tensor var_2913_cast_fp16 = transpose(perm = var_2913_perm_0, x = x_365_cast_fp16)[name = string("transpose_196")]; + tensor input_867_cast_fp16 = reshape(shape = var_2914, x = var_2913_cast_fp16)[name = string("input_867_cast_fp16")]; + tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282514560))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282645696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283203968))))[name = string("module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_867_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = string("input_871_cast_fp16")]; + tensor x_369_axes_0 = const()[name = string("x_369_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283220416)))]; + tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283222528)))]; + tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = string("x_369_cast_fp16")]; + tensor input_873_perm_0 = const()[name = string("input_873_perm_0"), val = tensor([0, 2, 1])]; + string input_875_pad_type_0 = const()[name = string("input_875_pad_type_0"), val = string("valid")]; + tensor input_875_strides_0 = const()[name = string("input_875_strides_0"), val = tensor([1])]; + tensor input_875_pad_0 = const()[name = string("input_875_pad_0"), val = tensor([0, 0])]; + tensor input_875_dilations_0 = const()[name = string("input_875_dilations_0"), val = tensor([1])]; + int32 input_875_groups_0 = const()[name = string("input_875_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283224640))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283486848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284609216))))[name = string("module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = string("transpose_195")]; + tensor input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_873_cast_fp16)[name = string("input_875_cast_fp16")]; + int32 x_371_split_num_splits_0 = const()[name = string("x_371_split_num_splits_0"), val = int32(2)]; + int32 x_371_split_axis_0 = const()[name = string("x_371_split_axis_0"), val = int32(1)]; + tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = string("x_371_split_cast_fp16")]; + tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = string("x_371_split_1_sigmoid_cast_fp16")]; + tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = string("x_371_cast_fp16")]; + tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_328)[name = string("input_877_cast_fp16")]; + tensor input_879_pad_0 = const()[name = string("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_879_mode_0 = const()[name = string("input_879_mode_0"), val = string("constant")]; + fp16 const_177_to_fp16 = const()[name = string("const_177_to_fp16"), val = fp16(0x0p+0)]; + tensor input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = string("input_879_cast_fp16")]; + string input_881_pad_type_0 = const()[name = string("input_881_pad_type_0"), val = string("valid")]; + int32 input_881_groups_0 = const()[name = string("input_881_groups_0"), val = int32(1024)]; + tensor input_881_strides_0 = const()[name = string("input_881_strides_0"), val = tensor([1])]; + tensor input_881_pad_0 = const()[name = string("input_881_pad_0"), val = tensor([0, 0])]; + tensor input_881_dilations_0 = const()[name = string("input_881_dilations_0"), val = tensor([1])]; + tensor const_280_to_fp16 = const()[name = string("const_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284642048)))]; + tensor const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284660544)))]; + tensor input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16, x = input_879_cast_fp16)[name = string("input_883_cast_fp16")]; + tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = string("input_885_cast_fp16")]; + string x_373_pad_type_0 = const()[name = string("x_373_pad_type_0"), val = string("valid")]; + tensor x_373_strides_0 = const()[name = string("x_373_strides_0"), val = tensor([1])]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0])]; + tensor x_373_dilations_0 = const()[name = string("x_373_dilations_0"), val = tensor([1])]; + int32 x_373_groups_0 = const()[name = string("x_373_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284662656))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284793792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285350208))))[name = string("module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_885_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = string("transpose_194")]; + tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = string("input_889_cast_fp16")]; + tensor input_891_axes_0 = const()[name = string("input_891_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285366656)))]; + tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285368768)))]; + tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285370880))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285895232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288121664))))[name = string("module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_891_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288187264))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288711616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290881728))))[name = string("module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_895_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_2974_to_fp16 = const()[name = string("op_2974_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2975_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2974_to_fp16)[name = string("op_2975_cast_fp16")]; + tensor input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2975_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor input_903_axes_0 = const()[name = string("input_903_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = string("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290898176)))]; + tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = string("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290900288)))]; + tensor input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = string("input_903_cast_fp16")]; + tensor input_905_axes_0 = const()[name = string("input_905_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290902400)))]; + tensor module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290904512)))]; + tensor input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = string("input_905_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290906624))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291430976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293640512))))[name = string("module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_905_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293706112))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294230464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296388288))))[name = string("module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_909_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_3003_to_fp16 = const()[name = string("op_3003_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3004_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3003_to_fp16)[name = string("op_3004_cast_fp16")]; + tensor input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3004_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296404736)))]; + tensor module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296406848)))]; + tensor query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = string("query_35_cast_fp16")]; + tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296408960))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296540096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297100480))))[name = string("module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_3020 = const()[name = string("op_3020"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_3020, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297116928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297248064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297810304))))[name = string("module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_3024 = const()[name = string("op_3024"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_3024, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297826752))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297957888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298516992))))[name = string("module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_3028 = const()[name = string("op_3028"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_3028, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298533440)))]; + tensor var_3040_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3040_cast_fp16")]; + tensor module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298535552)))]; + tensor var_3042_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3042_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_381_transpose_x_0 = const()[name = string("x_381_transpose_x_0"), val = bool(false)]; + bool x_381_transpose_y_0 = const()[name = string("x_381_transpose_y_0"), val = bool(false)]; + tensor var_3044_to_fp16 = const()[name = string("op_3044_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298537664)))]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3042_cast_fp16)[name = string("transpose_193")]; + tensor x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = var_3044_to_fp16)[name = string("x_381_cast_fp16")]; + tensor x_383_pad_0 = const()[name = string("x_383_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_383_mode_0 = const()[name = string("x_383_mode_0"), val = string("constant")]; + fp16 const_184_to_fp16 = const()[name = string("const_184_to_fp16"), val = fp16(0x0p+0)]; + tensor x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor var_3052 = const()[name = string("op_3052"), val = tensor([1, 8, -1, 188])]; + tensor x_385_cast_fp16 = reshape(shape = var_3052, x = x_383_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor var_3056_begin_0 = const()[name = string("op_3056_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3056_end_0 = const()[name = string("op_3056_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3056_end_mask_0 = const()[name = string("op_3056_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3056_cast_fp16 = slice_by_index(begin = var_3056_begin_0, end = var_3056_end_0, end_mask = var_3056_end_mask_0, x = x_385_cast_fp16)[name = string("op_3056_cast_fp16")]; + tensor var_3057 = const()[name = string("op_3057"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3057, x = var_3056_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3040_cast_fp16)[name = string("transpose_192")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_3066_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3066_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_3066_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = string("scores_71_cast_fp16")]; + tensor var_3072_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = string("op_3072_cast_fp16")]; + tensor input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3072_cast_fp16, cond = mask_3)[name = string("input_917_cast_fp16")]; + bool x_387_transpose_x_0 = const()[name = string("x_387_transpose_x_0"), val = bool(false)]; + bool x_387_transpose_y_0 = const()[name = string("x_387_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")]; + tensor x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_37_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor var_3076_perm_0 = const()[name = string("op_3076_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3077 = const()[name = string("op_3077"), val = tensor([1, -1, 1024])]; + tensor var_3076_cast_fp16 = transpose(perm = var_3076_perm_0, x = x_387_cast_fp16)[name = string("transpose_189")]; + tensor input_919_cast_fp16 = reshape(shape = var_3077, x = var_3076_cast_fp16)[name = string("input_919_cast_fp16")]; + tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299305728))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299436864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299996160))))[name = string("module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_919_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = string("input_923_cast_fp16")]; + tensor x_391_axes_0 = const()[name = string("x_391_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("module_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300012608)))]; + tensor module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("module_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300014720)))]; + tensor x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = string("x_391_cast_fp16")]; + tensor input_925_perm_0 = const()[name = string("input_925_perm_0"), val = tensor([0, 2, 1])]; + string input_927_pad_type_0 = const()[name = string("input_927_pad_type_0"), val = string("valid")]; + tensor input_927_strides_0 = const()[name = string("input_927_strides_0"), val = tensor([1])]; + tensor input_927_pad_0 = const()[name = string("input_927_pad_0"), val = tensor([0, 0])]; + tensor input_927_dilations_0 = const()[name = string("input_927_dilations_0"), val = tensor([1])]; + int32 input_927_groups_0 = const()[name = string("input_927_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300016832))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300279040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301405312))))[name = string("module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = string("transpose_188")]; + tensor input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_925_cast_fp16)[name = string("input_927_cast_fp16")]; + int32 x_393_split_num_splits_0 = const()[name = string("x_393_split_num_splits_0"), val = int32(2)]; + int32 x_393_split_axis_0 = const()[name = string("x_393_split_axis_0"), val = int32(1)]; + tensor x_393_split_cast_fp16_0, tensor x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = string("x_393_split_cast_fp16")]; + tensor x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = string("x_393_split_1_sigmoid_cast_fp16")]; + tensor x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = string("x_393_cast_fp16")]; + tensor input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_328)[name = string("input_929_cast_fp16")]; + tensor input_931_pad_0 = const()[name = string("input_931_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_931_mode_0 = const()[name = string("input_931_mode_0"), val = string("constant")]; + fp16 const_187_to_fp16 = const()[name = string("const_187_to_fp16"), val = fp16(0x0p+0)]; + tensor input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = string("input_931_cast_fp16")]; + string input_933_pad_type_0 = const()[name = string("input_933_pad_type_0"), val = string("valid")]; + int32 input_933_groups_0 = const()[name = string("input_933_groups_0"), val = int32(1024)]; + tensor input_933_strides_0 = const()[name = string("input_933_strides_0"), val = tensor([1])]; + tensor input_933_pad_0 = const()[name = string("input_933_pad_0"), val = tensor([0, 0])]; + tensor input_933_dilations_0 = const()[name = string("input_933_dilations_0"), val = tensor([1])]; + tensor const_282_to_fp16 = const()[name = string("const_282_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301438144)))]; + tensor const_283_to_fp16 = const()[name = string("const_283_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301456640)))]; + tensor input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16, x = input_931_cast_fp16)[name = string("input_935_cast_fp16")]; + tensor input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = string("input_937_cast_fp16")]; + string x_395_pad_type_0 = const()[name = string("x_395_pad_type_0"), val = string("valid")]; + tensor x_395_strides_0 = const()[name = string("x_395_strides_0"), val = tensor([1])]; + tensor x_395_pad_0 = const()[name = string("x_395_pad_0"), val = tensor([0, 0])]; + tensor x_395_dilations_0 = const()[name = string("x_395_dilations_0"), val = tensor([1])]; + int32 x_395_groups_0 = const()[name = string("x_395_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301458752))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301589888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302143040))))[name = string("module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_937_cast_fp16)[name = string("x_395_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = string("transpose_187")]; + tensor input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = string("input_941_cast_fp16")]; + tensor input_943_axes_0 = const()[name = string("input_943_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302159488)))]; + tensor module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302161600)))]; + tensor input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302163712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302688064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304917440))))[name = string("module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_943_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304983040))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305507392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307680704))))[name = string("module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_947_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_3137_to_fp16 = const()[name = string("op_3137_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3138_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3137_to_fp16)[name = string("op_3138_cast_fp16")]; + tensor input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3138_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor input_955_axes_0 = const()[name = string("input_955_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_out_weight_to_fp16 = const()[name = string("module_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307697152)))]; + tensor module_layers_17_norm_out_bias_to_fp16 = const()[name = string("module_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307699264)))]; + tensor input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = string("input_955_cast_fp16")]; + tensor input_957_axes_0 = const()[name = string("input_957_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307701376)))]; + tensor module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307703488)))]; + tensor input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = string("input_957_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307705600))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308229952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310438016))))[name = string("module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_957_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310503616))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311027968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313196800))))[name = string("module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_961_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3167_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3166_to_fp16)[name = string("op_3167_cast_fp16")]; + tensor input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3167_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313213248)))]; + tensor module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313215360)))]; + tensor query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = string("query_37_cast_fp16")]; + tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313217472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313348608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313906112))))[name = string("module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_3183 = const()[name = string("op_3183"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_3183, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313922560))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314053696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314615808))))[name = string("module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_3187 = const()[name = string("op_3187"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_3187, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314632256))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314763392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315323008))))[name = string("module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_3191 = const()[name = string("op_3191"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_3191, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315339456)))]; + tensor var_3203_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3203_cast_fp16")]; + tensor module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315341568)))]; + tensor var_3205_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3205_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor var_3207_to_fp16 = const()[name = string("op_3207_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315343680)))]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3205_cast_fp16)[name = string("transpose_186")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = var_3207_to_fp16)[name = string("x_403_cast_fp16")]; + tensor x_405_pad_0 = const()[name = string("x_405_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_405_mode_0 = const()[name = string("x_405_mode_0"), val = string("constant")]; + fp16 const_194_to_fp16 = const()[name = string("const_194_to_fp16"), val = fp16(0x0p+0)]; + tensor x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")]; + tensor var_3215 = const()[name = string("op_3215"), val = tensor([1, 8, -1, 188])]; + tensor x_407_cast_fp16 = reshape(shape = var_3215, x = x_405_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor var_3219_begin_0 = const()[name = string("op_3219_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3219_end_0 = const()[name = string("op_3219_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3219_end_mask_0 = const()[name = string("op_3219_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3219_cast_fp16 = slice_by_index(begin = var_3219_begin_0, end = var_3219_end_0, end_mask = var_3219_end_mask_0, x = x_407_cast_fp16)[name = string("op_3219_cast_fp16")]; + tensor var_3220 = const()[name = string("op_3220"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_3220, x = var_3219_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3203_cast_fp16)[name = string("transpose_185")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_3229_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3229_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_3229_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = string("scores_75_cast_fp16")]; + tensor var_3235_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = string("op_3235_cast_fp16")]; + tensor input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3235_cast_fp16, cond = mask_3)[name = string("input_969_cast_fp16")]; + bool x_409_transpose_x_0 = const()[name = string("x_409_transpose_x_0"), val = bool(false)]; + bool x_409_transpose_y_0 = const()[name = string("x_409_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")]; + tensor x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_39_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor var_3239_perm_0 = const()[name = string("op_3239_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3240 = const()[name = string("op_3240"), val = tensor([1, -1, 1024])]; + tensor var_3239_cast_fp16 = transpose(perm = var_3239_perm_0, x = x_409_cast_fp16)[name = string("transpose_182")]; + tensor input_971_cast_fp16 = reshape(shape = var_3240, x = var_3239_cast_fp16)[name = string("input_971_cast_fp16")]; + tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316111744))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316242880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316798528))))[name = string("module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_971_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = string("input_975_cast_fp16")]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("module_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316814976)))]; + tensor module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("module_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316817088)))]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_977_perm_0 = const()[name = string("input_977_perm_0"), val = tensor([0, 2, 1])]; + string input_979_pad_type_0 = const()[name = string("input_979_pad_type_0"), val = string("valid")]; + tensor input_979_strides_0 = const()[name = string("input_979_strides_0"), val = tensor([1])]; + tensor input_979_pad_0 = const()[name = string("input_979_pad_0"), val = tensor([0, 0])]; + tensor input_979_dilations_0 = const()[name = string("input_979_dilations_0"), val = tensor([1])]; + int32 input_979_groups_0 = const()[name = string("input_979_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316819200))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317081408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318206144))))[name = string("module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = string("transpose_181")]; + tensor input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_977_cast_fp16)[name = string("input_979_cast_fp16")]; + int32 x_415_split_num_splits_0 = const()[name = string("x_415_split_num_splits_0"), val = int32(2)]; + int32 x_415_split_axis_0 = const()[name = string("x_415_split_axis_0"), val = int32(1)]; + tensor x_415_split_cast_fp16_0, tensor x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = string("x_415_split_cast_fp16")]; + tensor x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = string("x_415_split_1_sigmoid_cast_fp16")]; + tensor x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_328)[name = string("input_981_cast_fp16")]; + tensor input_983_pad_0 = const()[name = string("input_983_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_983_mode_0 = const()[name = string("input_983_mode_0"), val = string("constant")]; + fp16 const_197_to_fp16 = const()[name = string("const_197_to_fp16"), val = fp16(0x0p+0)]; + tensor input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = string("input_983_cast_fp16")]; + string input_985_pad_type_0 = const()[name = string("input_985_pad_type_0"), val = string("valid")]; + int32 input_985_groups_0 = const()[name = string("input_985_groups_0"), val = int32(1024)]; + tensor input_985_strides_0 = const()[name = string("input_985_strides_0"), val = tensor([1])]; + tensor input_985_pad_0 = const()[name = string("input_985_pad_0"), val = tensor([0, 0])]; + tensor input_985_dilations_0 = const()[name = string("input_985_dilations_0"), val = tensor([1])]; + tensor const_284_to_fp16 = const()[name = string("const_284_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318238976)))]; + tensor const_285_to_fp16 = const()[name = string("const_285_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318257472)))]; + tensor input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16, x = input_983_cast_fp16)[name = string("input_987_cast_fp16")]; + tensor input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = string("input_989_cast_fp16")]; + string x_417_pad_type_0 = const()[name = string("x_417_pad_type_0"), val = string("valid")]; + tensor x_417_strides_0 = const()[name = string("x_417_strides_0"), val = tensor([1])]; + tensor x_417_pad_0 = const()[name = string("x_417_pad_0"), val = tensor([0, 0])]; + tensor x_417_dilations_0 = const()[name = string("x_417_dilations_0"), val = tensor([1])]; + int32 x_417_groups_0 = const()[name = string("x_417_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318259584))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318390720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318944704))))[name = string("module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_989_cast_fp16)[name = string("x_417_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = string("transpose_180")]; + tensor input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = string("input_993_cast_fp16")]; + tensor input_995_axes_0 = const()[name = string("input_995_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318961152)))]; + tensor module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318963264)))]; + tensor input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318965376))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319489728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321710592))))[name = string("module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_995_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321776192))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322300544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324519040))))[name = string("module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_999_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_3300_to_fp16 = const()[name = string("op_3300_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3301_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3300_to_fp16)[name = string("op_3301_cast_fp16")]; + tensor input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3301_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor input_1007_axes_0 = const()[name = string("input_1007_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_out_weight_to_fp16 = const()[name = string("module_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324535488)))]; + tensor module_layers_18_norm_out_bias_to_fp16 = const()[name = string("module_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324537600)))]; + tensor input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = string("input_1007_cast_fp16")]; + tensor input_1009_axes_0 = const()[name = string("input_1009_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324539712)))]; + tensor module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324541824)))]; + tensor input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = string("input_1009_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324543936))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325068288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327279616))))[name = string("module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1009_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327345216))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327869568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330064576))))[name = string("module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1013_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_3329_to_fp16 = const()[name = string("op_3329_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3330_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3329_to_fp16)[name = string("op_3330_cast_fp16")]; + tensor input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3330_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330081024)))]; + tensor module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330083136)))]; + tensor query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = string("query_39_cast_fp16")]; + tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330085248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330216384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330773952))))[name = string("module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_3346 = const()[name = string("op_3346"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_3346, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330790400))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330921536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331485120))))[name = string("module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_3350 = const()[name = string("op_3350"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_3350, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331501568))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331632704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332193024))))[name = string("module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_3354 = const()[name = string("op_3354"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_3354, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332209472)))]; + tensor var_3366_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3366_cast_fp16")]; + tensor module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332211584)))]; + tensor var_3368_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3368_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_425_transpose_x_0 = const()[name = string("x_425_transpose_x_0"), val = bool(false)]; + bool x_425_transpose_y_0 = const()[name = string("x_425_transpose_y_0"), val = bool(false)]; + tensor var_3370_to_fp16 = const()[name = string("op_3370_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332213696)))]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3368_cast_fp16)[name = string("transpose_179")]; + tensor x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = var_3370_to_fp16)[name = string("x_425_cast_fp16")]; + tensor x_427_pad_0 = const()[name = string("x_427_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_427_mode_0 = const()[name = string("x_427_mode_0"), val = string("constant")]; + fp16 const_204_to_fp16 = const()[name = string("const_204_to_fp16"), val = fp16(0x0p+0)]; + tensor x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3378 = const()[name = string("op_3378"), val = tensor([1, 8, -1, 188])]; + tensor x_429_cast_fp16 = reshape(shape = var_3378, x = x_427_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3382_begin_0 = const()[name = string("op_3382_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3382_end_0 = const()[name = string("op_3382_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3382_end_mask_0 = const()[name = string("op_3382_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3382_cast_fp16 = slice_by_index(begin = var_3382_begin_0, end = var_3382_end_0, end_mask = var_3382_end_mask_0, x = x_429_cast_fp16)[name = string("op_3382_cast_fp16")]; + tensor var_3383 = const()[name = string("op_3383"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3383, x = var_3382_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3366_cast_fp16)[name = string("transpose_178")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_3392_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3392_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_3392_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = string("scores_79_cast_fp16")]; + tensor var_3398_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = string("op_3398_cast_fp16")]; + tensor input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3398_cast_fp16, cond = mask_3)[name = string("input_1021_cast_fp16")]; + bool x_431_transpose_x_0 = const()[name = string("x_431_transpose_x_0"), val = bool(false)]; + bool x_431_transpose_y_0 = const()[name = string("x_431_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")]; + tensor x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_41_cast_fp16)[name = string("x_431_cast_fp16")]; + tensor var_3402_perm_0 = const()[name = string("op_3402_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3403 = const()[name = string("op_3403"), val = tensor([1, -1, 1024])]; + tensor var_3402_cast_fp16 = transpose(perm = var_3402_perm_0, x = x_431_cast_fp16)[name = string("transpose_175")]; + tensor input_1023_cast_fp16 = reshape(shape = var_3403, x = var_3402_cast_fp16)[name = string("input_1023_cast_fp16")]; + tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332981760))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333112896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333672064))))[name = string("module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1023_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1027_cast_fp16")]; + tensor x_435_axes_0 = const()[name = string("x_435_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("module_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333688512)))]; + tensor module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("module_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333690624)))]; + tensor x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_1029_perm_0 = const()[name = string("input_1029_perm_0"), val = tensor([0, 2, 1])]; + string input_1031_pad_type_0 = const()[name = string("input_1031_pad_type_0"), val = string("valid")]; + tensor input_1031_strides_0 = const()[name = string("input_1031_strides_0"), val = tensor([1])]; + tensor input_1031_pad_0 = const()[name = string("input_1031_pad_0"), val = tensor([0, 0])]; + tensor input_1031_dilations_0 = const()[name = string("input_1031_dilations_0"), val = tensor([1])]; + int32 input_1031_groups_0 = const()[name = string("input_1031_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333692736))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333954944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335077568))))[name = string("module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = string("transpose_174")]; + tensor input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1029_cast_fp16)[name = string("input_1031_cast_fp16")]; + int32 x_437_split_num_splits_0 = const()[name = string("x_437_split_num_splits_0"), val = int32(2)]; + int32 x_437_split_axis_0 = const()[name = string("x_437_split_axis_0"), val = int32(1)]; + tensor x_437_split_cast_fp16_0, tensor x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = string("x_437_split_cast_fp16")]; + tensor x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = string("x_437_split_1_sigmoid_cast_fp16")]; + tensor x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_328)[name = string("input_1033_cast_fp16")]; + tensor input_1035_pad_0 = const()[name = string("input_1035_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1035_mode_0 = const()[name = string("input_1035_mode_0"), val = string("constant")]; + fp16 const_207_to_fp16 = const()[name = string("const_207_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = string("input_1035_cast_fp16")]; + string input_1037_pad_type_0 = const()[name = string("input_1037_pad_type_0"), val = string("valid")]; + int32 input_1037_groups_0 = const()[name = string("input_1037_groups_0"), val = int32(1024)]; + tensor input_1037_strides_0 = const()[name = string("input_1037_strides_0"), val = tensor([1])]; + tensor input_1037_pad_0 = const()[name = string("input_1037_pad_0"), val = tensor([0, 0])]; + tensor input_1037_dilations_0 = const()[name = string("input_1037_dilations_0"), val = tensor([1])]; + tensor const_286_to_fp16 = const()[name = string("const_286_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335110400)))]; + tensor const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335128896)))]; + tensor input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16, x = input_1035_cast_fp16)[name = string("input_1039_cast_fp16")]; + tensor input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = string("input_1041_cast_fp16")]; + string x_439_pad_type_0 = const()[name = string("x_439_pad_type_0"), val = string("valid")]; + tensor x_439_strides_0 = const()[name = string("x_439_strides_0"), val = tensor([1])]; + tensor x_439_pad_0 = const()[name = string("x_439_pad_0"), val = tensor([0, 0])]; + tensor x_439_dilations_0 = const()[name = string("x_439_dilations_0"), val = tensor([1])]; + int32 x_439_groups_0 = const()[name = string("x_439_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335131008))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335262144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335814592))))[name = string("module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1041_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = string("transpose_173")]; + tensor input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = string("input_1045_cast_fp16")]; + tensor input_1047_axes_0 = const()[name = string("input_1047_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335831040)))]; + tensor module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335833152)))]; + tensor input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335835264))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336359616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338589312))))[name = string("module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1047_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338654912))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339179264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341416640))))[name = string("module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1051_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_3463_to_fp16 = const()[name = string("op_3463_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3464_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3463_to_fp16)[name = string("op_3464_cast_fp16")]; + tensor input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3464_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor input_1059_axes_0 = const()[name = string("input_1059_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_out_weight_to_fp16 = const()[name = string("module_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341433088)))]; + tensor module_layers_19_norm_out_bias_to_fp16 = const()[name = string("module_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341435200)))]; + tensor input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = string("input_1059_cast_fp16")]; + tensor input_1061_axes_0 = const()[name = string("input_1061_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341437312)))]; + tensor module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341439424)))]; + tensor input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = string("input_1061_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341441536))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341965888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344179520))))[name = string("module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1061_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344245120))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344769472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346967616))))[name = string("module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1065_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_3492_to_fp16 = const()[name = string("op_3492_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3493_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3492_to_fp16)[name = string("op_3493_cast_fp16")]; + tensor input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3493_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346984064)))]; + tensor module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346986176)))]; + tensor query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = string("query_41_cast_fp16")]; + tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346988288))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347119424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347677696))))[name = string("module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_3509 = const()[name = string("op_3509"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_3509, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347694144))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347825280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348387008))))[name = string("module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_3513 = const()[name = string("op_3513"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_3513, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348403456))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348534592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349093760))))[name = string("module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_3517 = const()[name = string("op_3517"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_3517, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349110208)))]; + tensor var_3529_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_3529_cast_fp16")]; + tensor module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349112320)))]; + tensor var_3531_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_3531_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_447_transpose_x_0 = const()[name = string("x_447_transpose_x_0"), val = bool(false)]; + bool x_447_transpose_y_0 = const()[name = string("x_447_transpose_y_0"), val = bool(false)]; + tensor var_3533_to_fp16 = const()[name = string("op_3533_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349114432)))]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3531_cast_fp16)[name = string("transpose_172")]; + tensor x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = var_3533_to_fp16)[name = string("x_447_cast_fp16")]; + tensor x_449_pad_0 = const()[name = string("x_449_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_449_mode_0 = const()[name = string("x_449_mode_0"), val = string("constant")]; + fp16 const_214_to_fp16 = const()[name = string("const_214_to_fp16"), val = fp16(0x0p+0)]; + tensor x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")]; + tensor var_3541 = const()[name = string("op_3541"), val = tensor([1, 8, -1, 188])]; + tensor x_451_cast_fp16 = reshape(shape = var_3541, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_3545_begin_0 = const()[name = string("op_3545_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3545_end_0 = const()[name = string("op_3545_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3545_end_mask_0 = const()[name = string("op_3545_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3545_cast_fp16 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = x_451_cast_fp16)[name = string("op_3545_cast_fp16")]; + tensor var_3546 = const()[name = string("op_3546"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_3546, x = var_3545_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3529_cast_fp16)[name = string("transpose_171")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_3555_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_3555_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_3555_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = string("scores_83_cast_fp16")]; + tensor var_3561_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = string("op_3561_cast_fp16")]; + tensor input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3561_cast_fp16, cond = mask_3)[name = string("input_1073_cast_fp16")]; + bool x_453_transpose_x_0 = const()[name = string("x_453_transpose_x_0"), val = bool(false)]; + bool x_453_transpose_y_0 = const()[name = string("x_453_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")]; + tensor x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_43_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_3565_perm_0 = const()[name = string("op_3565_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3566 = const()[name = string("op_3566"), val = tensor([1, -1, 1024])]; + tensor var_3565_cast_fp16 = transpose(perm = var_3565_perm_0, x = x_453_cast_fp16)[name = string("transpose_168")]; + tensor input_1075_cast_fp16 = reshape(shape = var_3566, x = var_3565_cast_fp16)[name = string("input_1075_cast_fp16")]; + tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349882496))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350013632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350574080))))[name = string("module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1075_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1079_cast_fp16")]; + tensor x_457_axes_0 = const()[name = string("x_457_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("module_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350590528)))]; + tensor module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("module_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350592640)))]; + tensor x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = string("x_457_cast_fp16")]; + tensor input_1081_perm_0 = const()[name = string("input_1081_perm_0"), val = tensor([0, 2, 1])]; + string input_1083_pad_type_0 = const()[name = string("input_1083_pad_type_0"), val = string("valid")]; + tensor input_1083_strides_0 = const()[name = string("input_1083_strides_0"), val = tensor([1])]; + tensor input_1083_pad_0 = const()[name = string("input_1083_pad_0"), val = tensor([0, 0])]; + tensor input_1083_dilations_0 = const()[name = string("input_1083_dilations_0"), val = tensor([1])]; + int32 input_1083_groups_0 = const()[name = string("input_1083_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350594752))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350856960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351975552))))[name = string("module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = string("transpose_167")]; + tensor input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1081_cast_fp16)[name = string("input_1083_cast_fp16")]; + int32 x_459_split_num_splits_0 = const()[name = string("x_459_split_num_splits_0"), val = int32(2)]; + int32 x_459_split_axis_0 = const()[name = string("x_459_split_axis_0"), val = int32(1)]; + tensor x_459_split_cast_fp16_0, tensor x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = string("x_459_split_cast_fp16")]; + tensor x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = string("x_459_split_1_sigmoid_cast_fp16")]; + tensor x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_328)[name = string("input_1085_cast_fp16")]; + tensor input_1087_pad_0 = const()[name = string("input_1087_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1087_mode_0 = const()[name = string("input_1087_mode_0"), val = string("constant")]; + fp16 const_217_to_fp16 = const()[name = string("const_217_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = string("input_1087_cast_fp16")]; + string input_1089_pad_type_0 = const()[name = string("input_1089_pad_type_0"), val = string("valid")]; + int32 input_1089_groups_0 = const()[name = string("input_1089_groups_0"), val = int32(1024)]; + tensor input_1089_strides_0 = const()[name = string("input_1089_strides_0"), val = tensor([1])]; + tensor input_1089_pad_0 = const()[name = string("input_1089_pad_0"), val = tensor([0, 0])]; + tensor input_1089_dilations_0 = const()[name = string("input_1089_dilations_0"), val = tensor([1])]; + tensor const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352008384)))]; + tensor const_289_to_fp16 = const()[name = string("const_289_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352026880)))]; + tensor input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16, x = input_1087_cast_fp16)[name = string("input_1091_cast_fp16")]; + tensor input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = string("input_1093_cast_fp16")]; + string x_461_pad_type_0 = const()[name = string("x_461_pad_type_0"), val = string("valid")]; + tensor x_461_strides_0 = const()[name = string("x_461_strides_0"), val = tensor([1])]; + tensor x_461_pad_0 = const()[name = string("x_461_pad_0"), val = tensor([0, 0])]; + tensor x_461_dilations_0 = const()[name = string("x_461_dilations_0"), val = tensor([1])]; + int32 x_461_groups_0 = const()[name = string("x_461_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352028992))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352160128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352710912))))[name = string("module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1093_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = string("transpose_166")]; + tensor input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = string("input_1097_cast_fp16")]; + tensor input_1099_axes_0 = const()[name = string("input_1099_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352727360)))]; + tensor module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352729472)))]; + tensor input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352731584))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353255936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355485952))))[name = string("module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1099_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355551552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356075904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358301312))))[name = string("module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1103_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_3626_to_fp16 = const()[name = string("op_3626_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3627_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3626_to_fp16)[name = string("op_3627_cast_fp16")]; + tensor input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3627_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor input_1111_axes_0 = const()[name = string("input_1111_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_out_weight_to_fp16 = const()[name = string("module_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358317760)))]; + tensor module_layers_20_norm_out_bias_to_fp16 = const()[name = string("module_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358319872)))]; + tensor input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = string("input_1111_cast_fp16")]; + tensor input_1113_axes_0 = const()[name = string("input_1113_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358321984)))]; + tensor module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358324096)))]; + tensor input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = string("input_1113_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358326208))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358850560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361067328))))[name = string("module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1113_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361132928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361657280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363820992))))[name = string("module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1117_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_3655_to_fp16 = const()[name = string("op_3655_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3656_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3655_to_fp16)[name = string("op_3656_cast_fp16")]; + tensor input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3656_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363837440)))]; + tensor module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363839552)))]; + tensor query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = string("query_43_cast_fp16")]; + tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363841664))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363972800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364530432))))[name = string("module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_3672 = const()[name = string("op_3672"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_3672, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364546880))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364678016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365240128))))[name = string("module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_3676 = const()[name = string("op_3676"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_3676, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365256576))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365387712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365947072))))[name = string("module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_3680 = const()[name = string("op_3680"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_3680, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365963520)))]; + tensor var_3692_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_3692_cast_fp16")]; + tensor module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365965632)))]; + tensor var_3694_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_3694_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_469_transpose_x_0 = const()[name = string("x_469_transpose_x_0"), val = bool(false)]; + bool x_469_transpose_y_0 = const()[name = string("x_469_transpose_y_0"), val = bool(false)]; + tensor var_3696_to_fp16 = const()[name = string("op_3696_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365967744)))]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3694_cast_fp16)[name = string("transpose_165")]; + tensor x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = var_3696_to_fp16)[name = string("x_469_cast_fp16")]; + tensor x_471_pad_0 = const()[name = string("x_471_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_471_mode_0 = const()[name = string("x_471_mode_0"), val = string("constant")]; + fp16 const_224_to_fp16 = const()[name = string("const_224_to_fp16"), val = fp16(0x0p+0)]; + tensor x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")]; + tensor var_3704 = const()[name = string("op_3704"), val = tensor([1, 8, -1, 188])]; + tensor x_473_cast_fp16 = reshape(shape = var_3704, x = x_471_cast_fp16)[name = string("x_473_cast_fp16")]; + tensor var_3708_begin_0 = const()[name = string("op_3708_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3708_end_0 = const()[name = string("op_3708_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3708_end_mask_0 = const()[name = string("op_3708_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3708_cast_fp16 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = x_473_cast_fp16)[name = string("op_3708_cast_fp16")]; + tensor var_3709 = const()[name = string("op_3709"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_3709, x = var_3708_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3692_cast_fp16)[name = string("transpose_164")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_3718_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_3718_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_3718_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = string("scores_87_cast_fp16")]; + tensor var_3724_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = string("op_3724_cast_fp16")]; + tensor input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3724_cast_fp16, cond = mask_3)[name = string("input_1125_cast_fp16")]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_45_cast_fp16)[name = string("x_475_cast_fp16")]; + tensor var_3728_perm_0 = const()[name = string("op_3728_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3729 = const()[name = string("op_3729"), val = tensor([1, -1, 1024])]; + tensor var_3728_cast_fp16 = transpose(perm = var_3728_perm_0, x = x_475_cast_fp16)[name = string("transpose_161")]; + tensor input_1127_cast_fp16 = reshape(shape = var_3729, x = var_3728_cast_fp16)[name = string("input_1127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366735808))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366866944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367426688))))[name = string("module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1127_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1131_cast_fp16")]; + tensor x_479_axes_0 = const()[name = string("x_479_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("module_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367443136)))]; + tensor module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("module_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367445248)))]; + tensor x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor input_1133_perm_0 = const()[name = string("input_1133_perm_0"), val = tensor([0, 2, 1])]; + string input_1135_pad_type_0 = const()[name = string("input_1135_pad_type_0"), val = string("valid")]; + tensor input_1135_strides_0 = const()[name = string("input_1135_strides_0"), val = tensor([1])]; + tensor input_1135_pad_0 = const()[name = string("input_1135_pad_0"), val = tensor([0, 0])]; + tensor input_1135_dilations_0 = const()[name = string("input_1135_dilations_0"), val = tensor([1])]; + int32 input_1135_groups_0 = const()[name = string("input_1135_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367447360))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367709568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368828352))))[name = string("module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = string("transpose_160")]; + tensor input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1133_cast_fp16)[name = string("input_1135_cast_fp16")]; + int32 x_481_split_num_splits_0 = const()[name = string("x_481_split_num_splits_0"), val = int32(2)]; + int32 x_481_split_axis_0 = const()[name = string("x_481_split_axis_0"), val = int32(1)]; + tensor x_481_split_cast_fp16_0, tensor x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = string("x_481_split_cast_fp16")]; + tensor x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = string("x_481_split_1_sigmoid_cast_fp16")]; + tensor x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_328)[name = string("input_1137_cast_fp16")]; + tensor input_1139_pad_0 = const()[name = string("input_1139_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1139_mode_0 = const()[name = string("input_1139_mode_0"), val = string("constant")]; + fp16 const_227_to_fp16 = const()[name = string("const_227_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = string("input_1139_cast_fp16")]; + string input_1141_pad_type_0 = const()[name = string("input_1141_pad_type_0"), val = string("valid")]; + int32 input_1141_groups_0 = const()[name = string("input_1141_groups_0"), val = int32(1024)]; + tensor input_1141_strides_0 = const()[name = string("input_1141_strides_0"), val = tensor([1])]; + tensor input_1141_pad_0 = const()[name = string("input_1141_pad_0"), val = tensor([0, 0])]; + tensor input_1141_dilations_0 = const()[name = string("input_1141_dilations_0"), val = tensor([1])]; + tensor const_290_to_fp16 = const()[name = string("const_290_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368861184)))]; + tensor const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368879680)))]; + tensor input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16, x = input_1139_cast_fp16)[name = string("input_1143_cast_fp16")]; + tensor input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = string("input_1145_cast_fp16")]; + string x_483_pad_type_0 = const()[name = string("x_483_pad_type_0"), val = string("valid")]; + tensor x_483_strides_0 = const()[name = string("x_483_strides_0"), val = tensor([1])]; + tensor x_483_pad_0 = const()[name = string("x_483_pad_0"), val = tensor([0, 0])]; + tensor x_483_dilations_0 = const()[name = string("x_483_dilations_0"), val = tensor([1])]; + int32 x_483_groups_0 = const()[name = string("x_483_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368881792))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369012928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369563648))))[name = string("module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1145_cast_fp16)[name = string("x_483_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = string("transpose_159")]; + tensor input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = string("input_1149_cast_fp16")]; + tensor input_1151_axes_0 = const()[name = string("input_1151_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369580096)))]; + tensor module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369582208)))]; + tensor input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369584320))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370108672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372344768))))[name = string("module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1151_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372410368))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372934720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375133184))))[name = string("module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1155_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_3789_to_fp16 = const()[name = string("op_3789_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3790_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3789_to_fp16)[name = string("op_3790_cast_fp16")]; + tensor input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3790_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor input_1163_axes_0 = const()[name = string("input_1163_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_out_weight_to_fp16 = const()[name = string("module_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375149632)))]; + tensor module_layers_21_norm_out_bias_to_fp16 = const()[name = string("module_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375151744)))]; + tensor input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = string("input_1163_cast_fp16")]; + tensor input_1165_axes_0 = const()[name = string("input_1165_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375153856)))]; + tensor module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375155968)))]; + tensor input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = string("input_1165_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375158080))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375682432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377911424))))[name = string("module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1165_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377977024))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378501376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380658368))))[name = string("module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1169_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_3818_to_fp16 = const()[name = string("op_3818_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3819_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3818_to_fp16)[name = string("op_3819_cast_fp16")]; + tensor input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3819_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380674816)))]; + tensor module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380676928)))]; + tensor query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = string("query_45_cast_fp16")]; + tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380679040))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380810176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381368384))))[name = string("module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_3835 = const()[name = string("op_3835"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_3835, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381384832))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381515968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382076800))))[name = string("module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_3839 = const()[name = string("op_3839"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_3839, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382093248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382224384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382781888))))[name = string("module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_3843 = const()[name = string("op_3843"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_3843, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382798336)))]; + tensor var_3855_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_3855_cast_fp16")]; + tensor module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382800448)))]; + tensor var_3857_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_3857_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_491_transpose_x_0 = const()[name = string("x_491_transpose_x_0"), val = bool(false)]; + bool x_491_transpose_y_0 = const()[name = string("x_491_transpose_y_0"), val = bool(false)]; + tensor var_3859_to_fp16 = const()[name = string("op_3859_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382802560)))]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3857_cast_fp16)[name = string("transpose_158")]; + tensor x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = var_3859_to_fp16)[name = string("x_491_cast_fp16")]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_493_mode_0 = const()[name = string("x_493_mode_0"), val = string("constant")]; + fp16 const_234_to_fp16 = const()[name = string("const_234_to_fp16"), val = fp16(0x0p+0)]; + tensor x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor var_3867 = const()[name = string("op_3867"), val = tensor([1, 8, -1, 188])]; + tensor x_495_cast_fp16 = reshape(shape = var_3867, x = x_493_cast_fp16)[name = string("x_495_cast_fp16")]; + tensor var_3871_begin_0 = const()[name = string("op_3871_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3871_end_0 = const()[name = string("op_3871_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3871_end_mask_0 = const()[name = string("op_3871_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3871_cast_fp16 = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = x_495_cast_fp16)[name = string("op_3871_cast_fp16")]; + tensor var_3872 = const()[name = string("op_3872"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_3872, x = var_3871_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3855_cast_fp16)[name = string("transpose_157")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_3881_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_3881_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_3881_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = string("scores_91_cast_fp16")]; + tensor var_3887_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = string("op_3887_cast_fp16")]; + tensor input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_3887_cast_fp16, cond = mask_3)[name = string("input_1177_cast_fp16")]; + bool x_497_transpose_x_0 = const()[name = string("x_497_transpose_x_0"), val = bool(false)]; + bool x_497_transpose_y_0 = const()[name = string("x_497_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")]; + tensor x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_47_cast_fp16)[name = string("x_497_cast_fp16")]; + tensor var_3891_perm_0 = const()[name = string("op_3891_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3892 = const()[name = string("op_3892"), val = tensor([1, -1, 1024])]; + tensor var_3891_cast_fp16 = transpose(perm = var_3891_perm_0, x = x_497_cast_fp16)[name = string("transpose_154")]; + tensor input_1179_cast_fp16 = reshape(shape = var_3892, x = var_3891_cast_fp16)[name = string("input_1179_cast_fp16")]; + tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383570624))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383701760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384256512))))[name = string("module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1179_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1183_cast_fp16")]; + tensor x_501_axes_0 = const()[name = string("x_501_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("module_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384272960)))]; + tensor module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("module_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384275072)))]; + tensor x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = string("x_501_cast_fp16")]; + tensor input_1185_perm_0 = const()[name = string("input_1185_perm_0"), val = tensor([0, 2, 1])]; + string input_1187_pad_type_0 = const()[name = string("input_1187_pad_type_0"), val = string("valid")]; + tensor input_1187_strides_0 = const()[name = string("input_1187_strides_0"), val = tensor([1])]; + tensor input_1187_pad_0 = const()[name = string("input_1187_pad_0"), val = tensor([0, 0])]; + tensor input_1187_dilations_0 = const()[name = string("input_1187_dilations_0"), val = tensor([1])]; + int32 input_1187_groups_0 = const()[name = string("input_1187_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384277184))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384539392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385656448))))[name = string("module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = string("transpose_153")]; + tensor input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1185_cast_fp16)[name = string("input_1187_cast_fp16")]; + int32 x_503_split_num_splits_0 = const()[name = string("x_503_split_num_splits_0"), val = int32(2)]; + int32 x_503_split_axis_0 = const()[name = string("x_503_split_axis_0"), val = int32(1)]; + tensor x_503_split_cast_fp16_0, tensor x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = string("x_503_split_cast_fp16")]; + tensor x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = string("x_503_split_1_sigmoid_cast_fp16")]; + tensor x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_328)[name = string("input_1189_cast_fp16")]; + tensor input_1191_pad_0 = const()[name = string("input_1191_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1191_mode_0 = const()[name = string("input_1191_mode_0"), val = string("constant")]; + fp16 const_237_to_fp16 = const()[name = string("const_237_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = string("input_1191_cast_fp16")]; + string input_1193_pad_type_0 = const()[name = string("input_1193_pad_type_0"), val = string("valid")]; + int32 input_1193_groups_0 = const()[name = string("input_1193_groups_0"), val = int32(1024)]; + tensor input_1193_strides_0 = const()[name = string("input_1193_strides_0"), val = tensor([1])]; + tensor input_1193_pad_0 = const()[name = string("input_1193_pad_0"), val = tensor([0, 0])]; + tensor input_1193_dilations_0 = const()[name = string("input_1193_dilations_0"), val = tensor([1])]; + tensor const_292_to_fp16 = const()[name = string("const_292_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385689280)))]; + tensor const_293_to_fp16 = const()[name = string("const_293_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385707776)))]; + tensor input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16, x = input_1191_cast_fp16)[name = string("input_1195_cast_fp16")]; + tensor input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = string("input_1197_cast_fp16")]; + string x_505_pad_type_0 = const()[name = string("x_505_pad_type_0"), val = string("valid")]; + tensor x_505_strides_0 = const()[name = string("x_505_strides_0"), val = tensor([1])]; + tensor x_505_pad_0 = const()[name = string("x_505_pad_0"), val = tensor([0, 0])]; + tensor x_505_dilations_0 = const()[name = string("x_505_dilations_0"), val = tensor([1])]; + int32 x_505_groups_0 = const()[name = string("x_505_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385709888))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385841024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386389312))))[name = string("module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1197_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = string("transpose_152")]; + tensor input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = string("input_1201_cast_fp16")]; + tensor input_1203_axes_0 = const()[name = string("input_1203_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386405760)))]; + tensor module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386407872)))]; + tensor input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386409984))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386934336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389159040))))[name = string("module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1203_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389224640))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389748992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391822720))))[name = string("module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1207_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_3952_to_fp16 = const()[name = string("op_3952_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3953_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3952_to_fp16)[name = string("op_3953_cast_fp16")]; + tensor input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3953_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor input_1215_axes_0 = const()[name = string("input_1215_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_out_weight_to_fp16 = const()[name = string("module_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391839168)))]; + tensor module_layers_22_norm_out_bias_to_fp16 = const()[name = string("module_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391841280)))]; + tensor input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = string("input_1215_cast_fp16")]; + tensor input_1217_axes_0 = const()[name = string("input_1217_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391843392)))]; + tensor module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391845504)))]; + tensor input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = string("input_1217_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391847616))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392371968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394846400))))[name = string("module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1217_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394912000))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395436352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397719232))))[name = string("module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1221_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_3981_to_fp16 = const()[name = string("op_3981_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3982_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3981_to_fp16)[name = string("op_3982_cast_fp16")]; + tensor input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3982_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor query_axes_0 = const()[name = string("query_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397735680)))]; + tensor module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397737792)))]; + tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = string("query_cast_fp16")]; + tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397739904))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397871040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398429440))))[name = string("module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_3998 = const()[name = string("op_3998"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_3998, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398445888))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398577024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399136064))))[name = string("module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_4002 = const()[name = string("op_4002"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_4002, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399152512))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399283648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399840960))))[name = string("module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_4006 = const()[name = string("op_4006"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_4006, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399857408)))]; + tensor var_4018_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4018_cast_fp16")]; + tensor module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399859520)))]; + tensor var_4020_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4020_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_513_transpose_x_0 = const()[name = string("x_513_transpose_x_0"), val = bool(false)]; + bool x_513_transpose_y_0 = const()[name = string("x_513_transpose_y_0"), val = bool(false)]; + tensor var_4022_to_fp16 = const()[name = string("op_4022_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399861632)))]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4020_cast_fp16)[name = string("transpose_151")]; + tensor x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_4022_to_fp16)[name = string("x_513_cast_fp16")]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_515_mode_0 = const()[name = string("x_515_mode_0"), val = string("constant")]; + fp16 const_244_to_fp16 = const()[name = string("const_244_to_fp16"), val = fp16(0x0p+0)]; + tensor x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor var_4030 = const()[name = string("op_4030"), val = tensor([1, 8, -1, 188])]; + tensor x_517_cast_fp16 = reshape(shape = var_4030, x = x_515_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor var_4034_begin_0 = const()[name = string("op_4034_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4034_end_0 = const()[name = string("op_4034_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_4034_end_mask_0 = const()[name = string("op_4034_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4034_cast_fp16 = slice_by_index(begin = var_4034_begin_0, end = var_4034_end_0, end_mask = var_4034_end_mask_0, x = x_517_cast_fp16)[name = string("op_4034_cast_fp16")]; + tensor var_4035 = const()[name = string("op_4035"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4035, x = var_4034_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4018_cast_fp16)[name = string("transpose_150")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_4044_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4044_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_4044_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = string("scores_cast_fp16")]; + tensor var_4050_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_4050_cast_fp16")]; + tensor input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4050_cast_fp16, cond = mask_3)[name = string("input_1229_cast_fp16")]; + bool x_519_transpose_x_0 = const()[name = string("x_519_transpose_x_0"), val = bool(false)]; + bool x_519_transpose_y_0 = const()[name = string("x_519_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")]; + tensor x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor var_4054_perm_0 = const()[name = string("op_4054_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4055 = const()[name = string("op_4055"), val = tensor([1, -1, 1024])]; + tensor var_4054_cast_fp16 = transpose(perm = var_4054_perm_0, x = x_519_cast_fp16)[name = string("transpose_147")]; + tensor input_1231_cast_fp16 = reshape(shape = var_4055, x = var_4054_cast_fp16)[name = string("input_1231_cast_fp16")]; + tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400629696))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400760832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(401323200))))[name = string("module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1231_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1235_cast_fp16")]; + tensor x_523_axes_0 = const()[name = string("x_523_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("module_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(401339648)))]; + tensor module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("module_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(401341760)))]; + tensor x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = string("x_523_cast_fp16")]; + tensor input_1237_perm_0 = const()[name = string("input_1237_perm_0"), val = tensor([0, 2, 1])]; + string input_1239_pad_type_0 = const()[name = string("input_1239_pad_type_0"), val = string("valid")]; + tensor input_1239_strides_0 = const()[name = string("input_1239_strides_0"), val = tensor([1])]; + tensor input_1239_pad_0 = const()[name = string("input_1239_pad_0"), val = tensor([0, 0])]; + tensor input_1239_dilations_0 = const()[name = string("input_1239_dilations_0"), val = tensor([1])]; + int32 input_1239_groups_0 = const()[name = string("input_1239_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(401343872))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(401606080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402728000))))[name = string("module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = string("transpose_146")]; + tensor input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1237_cast_fp16)[name = string("input_1239_cast_fp16")]; + int32 x_525_split_num_splits_0 = const()[name = string("x_525_split_num_splits_0"), val = int32(2)]; + int32 x_525_split_axis_0 = const()[name = string("x_525_split_axis_0"), val = int32(1)]; + tensor x_525_split_cast_fp16_0, tensor x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = string("x_525_split_cast_fp16")]; + tensor x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = string("x_525_split_1_sigmoid_cast_fp16")]; + tensor x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = string("x_525_cast_fp16")]; + tensor input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_328)[name = string("input_1241_cast_fp16")]; + tensor input_1243_pad_0 = const()[name = string("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1243_mode_0 = const()[name = string("input_1243_mode_0"), val = string("constant")]; + fp16 const_247_to_fp16 = const()[name = string("const_247_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = string("input_1243_cast_fp16")]; + string input_1245_pad_type_0 = const()[name = string("input_1245_pad_type_0"), val = string("valid")]; + int32 input_1245_groups_0 = const()[name = string("input_1245_groups_0"), val = int32(1024)]; + tensor input_1245_strides_0 = const()[name = string("input_1245_strides_0"), val = tensor([1])]; + tensor input_1245_pad_0 = const()[name = string("input_1245_pad_0"), val = tensor([0, 0])]; + tensor input_1245_dilations_0 = const()[name = string("input_1245_dilations_0"), val = tensor([1])]; + tensor const_294_to_fp16 = const()[name = string("const_294_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402760832)))]; + tensor const_295_to_fp16 = const()[name = string("const_295_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402779328)))]; + tensor input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16, x = input_1243_cast_fp16)[name = string("input_1247_cast_fp16")]; + tensor input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = string("input_1249_cast_fp16")]; + string x_527_pad_type_0 = const()[name = string("x_527_pad_type_0"), val = string("valid")]; + tensor x_527_strides_0 = const()[name = string("x_527_strides_0"), val = tensor([1])]; + tensor x_527_pad_0 = const()[name = string("x_527_pad_0"), val = tensor([0, 0])]; + tensor x_527_dilations_0 = const()[name = string("x_527_dilations_0"), val = tensor([1])]; + int32 x_527_groups_0 = const()[name = string("x_527_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402781440))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402912576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403462272))))[name = string("module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1249_cast_fp16)[name = string("x_527_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = string("transpose_145")]; + tensor input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = string("input_1253_cast_fp16")]; + tensor input_1255_axes_0 = const()[name = string("input_1255_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403478720)))]; + tensor module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403480832)))]; + tensor input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403482944))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404007296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406485504))))[name = string("module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1255_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406551104))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407075456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409430464))))[name = string("module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1259_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_4115_to_fp16 = const()[name = string("op_4115_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4116_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4115_to_fp16)[name = string("op_4116_cast_fp16")]; + tensor input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4116_cast_fp16)[name = string("input_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_out_weight_to_fp16 = const()[name = string("module_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409446912)))]; + tensor module_layers_23_norm_out_bias_to_fp16 = const()[name = string("module_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409449024)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")]; + tensor obj_1_perm_0 = const()[name = string("obj_1_perm_0"), val = tensor([0, 2, 1])]; + string obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")]; + tensor encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = string("cast_0")]; + } -> (encoder, encoder_length); +} \ No newline at end of file diff --git a/bark-3.0-clean/Encoder.mlmodelc/weights/weight.bin b/bark-3.0-clean/Encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3c8e7ada74c5be1a3ca3294fc079c688fce5256f --- /dev/null +++ b/bark-3.0-clean/Encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4216107d2214bd058aec94c2e0f9a7e8114f5274cf0c6a28ffb8f21ea56337eb +size 409451136 diff --git a/bark-3.0-clean/Encoder_lut3.mlmodelc/analytics/coremldata.bin b/bark-3.0-clean/Encoder_lut3.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cdfd3460305f308090cab53deca41d7fb1522ed2 --- /dev/null +++ b/bark-3.0-clean/Encoder_lut3.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b20435e51a1ec8f1791a99f105633e322b7ef7e52d295977c8aecb1c49d6c14d +size 243 diff --git a/bark-3.0-clean/Encoder_lut3.mlmodelc/coremldata.bin b/bark-3.0-clean/Encoder_lut3.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..80d7ae4294b976348f15ec75a1a21ed6379b19e6 --- /dev/null +++ b/bark-3.0-clean/Encoder_lut3.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:841e03898f8f830ef4edd6c71eb6bb3779ba5b83230e071760df09950d56392d +size 543 diff --git a/bark-3.0-clean/Encoder_lut3.mlmodelc/model.mil b/bark-3.0-clean/Encoder_lut3.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..94ef603628c39bb8f0216ca40d052bcec9533b3d --- /dev/null +++ b/bark-3.0-clean/Encoder_lut3.mlmodelc/model.mil @@ -0,0 +1,3404 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor mel, tensor mel_length) { + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + string var_86_to_fp16_dtype_0 = const()[name = string("op_86_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor mel_length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = mel_length)[name = string("cast_3")]; + tensor var_88_cast_fp16 = add(x = mel_length_to_fp16, y = var_87_promoted_to_fp16)[name = string("op_88_cast_fp16")]; + fp16 _inversed_90_y_0_to_fp16 = const()[name = string("_inversed_90_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = string("_inversed_90_cast_fp16")]; + fp16 var_91_to_fp16 = const()[name = string("op_91_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = string("lengths_1_cast_fp16")]; + tensor lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = string("lengths_3_cast_fp16")]; + fp16 var_95_promoted_to_fp16 = const()[name = string("op_95_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = string("op_96_cast_fp16")]; + fp16 _inversed_98_y_0_to_fp16 = const()[name = string("_inversed_98_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = string("_inversed_98_cast_fp16")]; + fp16 var_99_to_fp16 = const()[name = string("op_99_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = string("lengths_7_cast_fp16")]; + tensor lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = string("lengths_9_cast_fp16")]; + fp16 var_103_promoted_to_fp16 = const()[name = string("op_103_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = string("op_104_cast_fp16")]; + fp16 _inversed_106_y_0_to_fp16 = const()[name = string("_inversed_106_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = string("_inversed_106_cast_fp16")]; + fp16 var_107_to_fp16 = const()[name = string("op_107_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = string("lengths_13_cast_fp16")]; + tensor lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = string("lengths_cast_fp16")]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_2")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_315")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = string("input_1_cast_fp16")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([2, 2])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = string("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4736)))]; + tensor input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor([2, 2])]; + int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(256)]; + tensor input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = string("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5312)))]; + tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9984)))]; + tensor input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = string("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10560)))]; + tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141696)))]; + tensor input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")]; + tensor input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor([2, 2])]; + int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(256)]; + tensor input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = string("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142272)))]; + tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146944)))]; + tensor input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = string("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147520)))]; + tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278656)))]; + tensor input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_157_perm_0 = const()[name = string("op_157_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_158 = const()[name = string("op_158"), val = tensor([1, 188, -1])]; + tensor var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = string("transpose_314")]; + tensor input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor module_pre_encode_out_weight_to_fp16 = const()[name = string("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279232)))]; + tensor module_pre_encode_out_bias_to_fp16 = const()[name = string("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8667904)))]; + tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = string("linear_0_cast_fp16")]; + string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670016)))]; + tensor var_196_axes_0 = const()[name = string("op_196_axes_0"), val = tensor([-1])]; + tensor encoder_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = string("cast_1")]; + tensor var_196 = expand_dims(axes = var_196_axes_0, x = encoder_length)[name = string("op_196")]; + tensor pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = string("pad_mask_1")]; + tensor var_198_axes_0 = const()[name = string("op_198_axes_0"), val = tensor([1])]; + tensor var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = string("op_198")]; + tensor var_199 = const()[name = string("op_199"), val = tensor([1, 188, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = string("pad_mask_for_att_mask_1")]; + tensor var_201_perm_0 = const()[name = string("op_201_perm_0"), val = tensor([0, 2, 1])]; + tensor var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = string("pad_mask_for_att_mask")]; + tensor const_7 = const()[name = string("const_7"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = string("att_mask")]; + tensor mask_1 = logical_not(x = att_mask)[name = string("mask_1")]; + tensor pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")]; + tensor input_21_axes_0 = const()[name = string("input_21_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670848)))]; + tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8672960)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8675072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10248000))))[name = string("module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_1_bias_0_to_fp16 = const()[name = string("linear_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10313600)))]; + tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_21_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10321856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11894784))))[name = string("module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11911232)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_25_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_232_to_fp16 = const()[name = string("op_232_to_fp16"), val = fp16(0x1p-1)]; + tensor var_233_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_232_to_fp16)[name = string("op_233_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_233_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11913344)))]; + tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11915456)))]; + tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = string("query_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11917568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12310848))))[name = string("module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_249 = const()[name = string("op_249"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_249, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12327296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12720576))))[name = string("module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_253, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12737024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13130304))))[name = string("module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_257 = const()[name = string("op_257"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_257, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13146752)))]; + tensor var_269_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_269_cast_fp16")]; + tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13148864)))]; + tensor var_271_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_271_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor var_273_to_fp16 = const()[name = string("op_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13150976)))]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_271_cast_fp16)[name = string("transpose_312")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_273_to_fp16)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_281 = const()[name = string("op_281"), val = tensor([1, 8, -1, 188])]; + tensor x_11_cast_fp16 = reshape(shape = var_281, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_285_begin_0 = const()[name = string("op_285_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_285_end_0 = const()[name = string("op_285_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_285_end_mask_0 = const()[name = string("op_285_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_285_cast_fp16 = slice_by_index(begin = var_285_begin_0, end = var_285_end_0, end_mask = var_285_end_mask_0, x = x_11_cast_fp16)[name = string("op_285_cast_fp16")]; + tensor var_286 = const()[name = string("op_286"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_286, x = var_285_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_269_cast_fp16)[name = string("transpose_311")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_295_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_295_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_295_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_3_axes_0 = const()[name = string("mask_3_axes_0"), val = tensor([1])]; + tensor mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = string("mask_3")]; + fp16 var_12_to_fp16 = const()[name = string("op_12_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = string("scores_3_cast_fp16")]; + tensor var_301_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_301_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_301_cast_fp16, cond = mask_3)[name = string("input_33_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_305_perm_0 = const()[name = string("op_305_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_306 = const()[name = string("op_306"), val = tensor([1, -1, 1024])]; + tensor var_305_cast_fp16 = transpose(perm = var_305_perm_0, x = x_13_cast_fp16)[name = string("transpose_308")]; + tensor input_35_cast_fp16 = reshape(shape = var_306, x = var_305_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13919040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14312320))))[name = string("module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14328768)))]; + tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14330880)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_41_perm_0 = const()[name = string("input_41_perm_0"), val = tensor([0, 2, 1])]; + string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")]; + tensor input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor([1])]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0])]; + tensor input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor([1])]; + int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14332992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15119488))))[name = string("module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = string("transpose_307")]; + tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; + tensor var_328 = expand_dims(axes = var_328_axes_0, x = pad_mask)[name = string("op_328")]; + tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_328)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("constant")]; + fp16 const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1024)]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1])]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([0, 0])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([1])]; + tensor const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15152320)))]; + tensor const_249_to_fp16 = const()[name = string("const_249_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15170816)))]; + tensor input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15172928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15566208))))[name = string("module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_53_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_306")]; + tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor input_59_axes_0 = const()[name = string("input_59_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15582656)))]; + tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15584768)))]; + tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15586880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17159808))))[name = string("module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_59_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17225408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18798336))))[name = string("module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_63_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_366_to_fp16 = const()[name = string("op_366_to_fp16"), val = fp16(0x1p-1)]; + tensor var_367_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_366_to_fp16)[name = string("op_367_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_367_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = string("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18814784)))]; + tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = string("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18816896)))]; + tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor input_73_axes_0 = const()[name = string("input_73_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18819008)))]; + tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18821120)))]; + tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18823232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20396160))))[name = string("module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_73_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20461760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22034688))))[name = string("module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_77_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p-1)]; + tensor var_396_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_396_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22051136)))]; + tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22053248)))]; + tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = string("query_3_cast_fp16")]; + tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22055360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22448640))))[name = string("module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_412, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22465088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22858368))))[name = string("module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_416 = const()[name = string("op_416"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_416, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22874816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23268096))))[name = string("module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_420 = const()[name = string("op_420"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_420, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23284544)))]; + tensor var_432_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_432_cast_fp16")]; + tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23286656)))]; + tensor var_434_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_434_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_29_transpose_x_0 = const()[name = string("x_29_transpose_x_0"), val = bool(false)]; + bool x_29_transpose_y_0 = const()[name = string("x_29_transpose_y_0"), val = bool(false)]; + tensor var_436_to_fp16 = const()[name = string("op_436_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23288768)))]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_434_cast_fp16)[name = string("transpose_305")]; + tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_436_to_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_31_mode_0 = const()[name = string("x_31_mode_0"), val = string("constant")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_444 = const()[name = string("op_444"), val = tensor([1, 8, -1, 188])]; + tensor x_33_cast_fp16 = reshape(shape = var_444, x = x_31_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_448_begin_0 = const()[name = string("op_448_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_448_end_0 = const()[name = string("op_448_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_448_end_mask_0 = const()[name = string("op_448_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_448_cast_fp16 = slice_by_index(begin = var_448_begin_0, end = var_448_end_0, end_mask = var_448_end_mask_0, x = x_33_cast_fp16)[name = string("op_448_cast_fp16")]; + tensor var_449 = const()[name = string("op_449"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_449, x = var_448_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_432_cast_fp16)[name = string("transpose_304")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_458_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_458_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_458_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = string("scores_7_cast_fp16")]; + tensor var_464_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_464_cast_fp16, cond = mask_3)[name = string("input_85_cast_fp16")]; + bool x_35_transpose_x_0 = const()[name = string("x_35_transpose_x_0"), val = bool(false)]; + bool x_35_transpose_y_0 = const()[name = string("x_35_transpose_y_0"), val = bool(false)]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")]; + tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_468_perm_0 = const()[name = string("op_468_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_469 = const()[name = string("op_469"), val = tensor([1, -1, 1024])]; + tensor var_468_cast_fp16 = transpose(perm = var_468_perm_0, x = x_35_cast_fp16)[name = string("transpose_301")]; + tensor input_87_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24056832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24450112))))[name = string("module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_87_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24466560)))]; + tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24468672)))]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([1])]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([0, 0])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24470784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25257280))))[name = string("module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = string("transpose_300")]; + tensor input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")]; + int32 x_41_split_num_splits_0 = const()[name = string("x_41_split_num_splits_0"), val = int32(2)]; + int32 x_41_split_axis_0 = const()[name = string("x_41_split_axis_0"), val = int32(1)]; + tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = string("x_41_split_cast_fp16")]; + tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = string("x_41_split_1_sigmoid_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_328)[name = string("input_97_cast_fp16")]; + tensor input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("constant")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1024)]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1])]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([0, 0])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1])]; + tensor const_250_to_fp16 = const()[name = string("const_250_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25290112)))]; + tensor const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25308608)))]; + tensor input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16, x = input_99_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_43_pad_type_0 = const()[name = string("x_43_pad_type_0"), val = string("valid")]; + tensor x_43_strides_0 = const()[name = string("x_43_strides_0"), val = tensor([1])]; + tensor x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor([0, 0])]; + tensor x_43_dilations_0 = const()[name = string("x_43_dilations_0"), val = tensor([1])]; + int32 x_43_groups_0 = const()[name = string("x_43_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25310720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25704000))))[name = string("module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_105_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = string("transpose_299")]; + tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor input_111_axes_0 = const()[name = string("input_111_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25720448)))]; + tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25722560)))]; + tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25724672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27297600))))[name = string("module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_111_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27363200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28936128))))[name = string("module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_115_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1p-1)]; + tensor var_530_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_530_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor input_123_axes_0 = const()[name = string("input_123_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = string("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28952576)))]; + tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = string("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28954688)))]; + tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28956800)))]; + tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28958912)))]; + tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28961024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30533952))))[name = string("module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_125_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30599552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32172480))))[name = string("module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_129_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_558_to_fp16 = const()[name = string("op_558_to_fp16"), val = fp16(0x1p-1)]; + tensor var_559_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_558_to_fp16)[name = string("op_559_cast_fp16")]; + tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_559_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32188928)))]; + tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32191040)))]; + tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = string("query_5_cast_fp16")]; + tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32193152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32586432))))[name = string("module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_575 = const()[name = string("op_575"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_575, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32602880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32996160))))[name = string("module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_579 = const()[name = string("op_579"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_579, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33012608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33405888))))[name = string("module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_583 = const()[name = string("op_583"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_583, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33422336)))]; + tensor var_595_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_595_cast_fp16")]; + tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33424448)))]; + tensor var_597_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_597_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_51_transpose_x_0 = const()[name = string("x_51_transpose_x_0"), val = bool(false)]; + bool x_51_transpose_y_0 = const()[name = string("x_51_transpose_y_0"), val = bool(false)]; + tensor var_599_to_fp16 = const()[name = string("op_599_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33426560)))]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_597_cast_fp16)[name = string("transpose_298")]; + tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_599_to_fp16)[name = string("x_51_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_607 = const()[name = string("op_607"), val = tensor([1, 8, -1, 188])]; + tensor x_55_cast_fp16 = reshape(shape = var_607, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_611_begin_0 = const()[name = string("op_611_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_611_end_0 = const()[name = string("op_611_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_611_end_mask_0 = const()[name = string("op_611_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_611_cast_fp16 = slice_by_index(begin = var_611_begin_0, end = var_611_end_0, end_mask = var_611_end_mask_0, x = x_55_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor var_612 = const()[name = string("op_612"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_612, x = var_611_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_595_cast_fp16)[name = string("transpose_297")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_621_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_621_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_621_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = string("scores_11_cast_fp16")]; + tensor var_627_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_627_cast_fp16, cond = mask_3)[name = string("input_137_cast_fp16")]; + bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)]; + bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)]; + tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")]; + tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_631_perm_0 = const()[name = string("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_632 = const()[name = string("op_632"), val = tensor([1, -1, 1024])]; + tensor var_631_cast_fp16 = transpose(perm = var_631_perm_0, x = x_57_cast_fp16)[name = string("transpose_294")]; + tensor input_139_cast_fp16 = reshape(shape = var_632, x = var_631_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34194624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34587904))))[name = string("module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_139_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34604352)))]; + tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34606464)))]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34608576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35395072))))[name = string("module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = string("transpose_293")]; + tensor input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_145_cast_fp16)[name = string("input_147_cast_fp16")]; + int32 x_63_split_num_splits_0 = const()[name = string("x_63_split_num_splits_0"), val = int32(2)]; + int32 x_63_split_axis_0 = const()[name = string("x_63_split_axis_0"), val = int32(1)]; + tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = string("x_63_split_cast_fp16")]; + tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = string("x_63_split_1_sigmoid_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_328)[name = string("input_149_cast_fp16")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_151_mode_0 = const()[name = string("input_151_mode_0"), val = string("constant")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1024)]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + tensor const_252_to_fp16 = const()[name = string("const_252_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35427904)))]; + tensor const_253_to_fp16 = const()[name = string("const_253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35446400)))]; + tensor input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16, x = input_151_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("valid")]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([0, 0])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35448512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35841792))))[name = string("module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_157_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = string("transpose_292")]; + tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = string("input_161_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35858240)))]; + tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35860352)))]; + tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35862464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37435392))))[name = string("module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_163_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37500992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39073920))))[name = string("module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_167_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_692_to_fp16 = const()[name = string("op_692_to_fp16"), val = fp16(0x1p-1)]; + tensor var_693_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_692_to_fp16)[name = string("op_693_cast_fp16")]; + tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_693_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor input_175_axes_0 = const()[name = string("input_175_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = string("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39090368)))]; + tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = string("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39092480)))]; + tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor input_177_axes_0 = const()[name = string("input_177_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39094592)))]; + tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39096704)))]; + tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39098816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40671744))))[name = string("module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_177_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40737344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42310272))))[name = string("module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_181_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_721_to_fp16 = const()[name = string("op_721_to_fp16"), val = fp16(0x1p-1)]; + tensor var_722_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_721_to_fp16)[name = string("op_722_cast_fp16")]; + tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_722_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42326720)))]; + tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42328832)))]; + tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = string("query_7_cast_fp16")]; + tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42330944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42724224))))[name = string("module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_738, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42740672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43133952))))[name = string("module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_742, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43150400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43543680))))[name = string("module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43560128)))]; + tensor var_758_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_758_cast_fp16")]; + tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562240)))]; + tensor var_760_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_760_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_73_transpose_x_0 = const()[name = string("x_73_transpose_x_0"), val = bool(false)]; + bool x_73_transpose_y_0 = const()[name = string("x_73_transpose_y_0"), val = bool(false)]; + tensor var_762_to_fp16 = const()[name = string("op_762_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43564352)))]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_760_cast_fp16)[name = string("transpose_291")]; + tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_762_to_fp16)[name = string("x_73_cast_fp16")]; + tensor x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_770 = const()[name = string("op_770"), val = tensor([1, 8, -1, 188])]; + tensor x_77_cast_fp16 = reshape(shape = var_770, x = x_75_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_774_begin_0 = const()[name = string("op_774_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_774_end_0 = const()[name = string("op_774_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_774_end_mask_0 = const()[name = string("op_774_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = x_77_cast_fp16)[name = string("op_774_cast_fp16")]; + tensor var_775 = const()[name = string("op_775"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_758_cast_fp16)[name = string("transpose_290")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_784_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_784_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_784_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = string("scores_15_cast_fp16")]; + tensor var_790_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = string("op_790_cast_fp16")]; + tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_790_cast_fp16, cond = mask_3)[name = string("input_189_cast_fp16")]; + bool x_79_transpose_x_0 = const()[name = string("x_79_transpose_x_0"), val = bool(false)]; + bool x_79_transpose_y_0 = const()[name = string("x_79_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")]; + tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor var_794_perm_0 = const()[name = string("op_794_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_795 = const()[name = string("op_795"), val = tensor([1, -1, 1024])]; + tensor var_794_cast_fp16 = transpose(perm = var_794_perm_0, x = x_79_cast_fp16)[name = string("transpose_287")]; + tensor input_191_cast_fp16 = reshape(shape = var_795, x = var_794_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44332416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44725696))))[name = string("module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_191_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor x_83_axes_0 = const()[name = string("x_83_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44742144)))]; + tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44744256)))]; + tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_197_perm_0 = const()[name = string("input_197_perm_0"), val = tensor([0, 2, 1])]; + string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("valid")]; + tensor input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor([1])]; + tensor input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor([0, 0])]; + tensor input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor([1])]; + int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44746368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45532864))))[name = string("module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = string("transpose_286")]; + tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_197_cast_fp16)[name = string("input_199_cast_fp16")]; + int32 x_85_split_num_splits_0 = const()[name = string("x_85_split_num_splits_0"), val = int32(2)]; + int32 x_85_split_axis_0 = const()[name = string("x_85_split_axis_0"), val = int32(1)]; + tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = string("x_85_split_cast_fp16")]; + tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = string("x_85_split_1_sigmoid_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_328)[name = string("input_201_cast_fp16")]; + tensor input_203_pad_0 = const()[name = string("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("constant")]; + fp16 const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = fp16(0x0p+0)]; + tensor input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = string("input_203_cast_fp16")]; + string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("valid")]; + int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1024)]; + tensor input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor([1])]; + tensor input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor([0, 0])]; + tensor input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor([1])]; + tensor const_254_to_fp16 = const()[name = string("const_254_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45565696)))]; + tensor const_255_to_fp16 = const()[name = string("const_255_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45584192)))]; + tensor input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16, x = input_203_cast_fp16)[name = string("input_207_cast_fp16")]; + tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = string("input_209_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("valid")]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45586304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45979584))))[name = string("module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_209_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = string("transpose_285")]; + tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + tensor input_215_axes_0 = const()[name = string("input_215_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45996032)))]; + tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45998144)))]; + tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46000256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47573184))))[name = string("module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_215_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47638784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49211712))))[name = string("module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_219_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)]; + tensor var_856_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_856_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor input_227_axes_0 = const()[name = string("input_227_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = string("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49228160)))]; + tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = string("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49230272)))]; + tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = string("input_227_cast_fp16")]; + tensor input_229_axes_0 = const()[name = string("input_229_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49232384)))]; + tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49234496)))]; + tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49236608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50809536))))[name = string("module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_229_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50875136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52448064))))[name = string("module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_233_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p-1)]; + tensor var_885_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_885_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52464512)))]; + tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52466624)))]; + tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = string("query_9_cast_fp16")]; + tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52468736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52862016))))[name = string("module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_901 = const()[name = string("op_901"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_901, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52878464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53271744))))[name = string("module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_905 = const()[name = string("op_905"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_905, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53288192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53681472))))[name = string("module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_909 = const()[name = string("op_909"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_909, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53697920)))]; + tensor var_921_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_921_cast_fp16")]; + tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53700032)))]; + tensor var_923_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_923_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_95_transpose_x_0 = const()[name = string("x_95_transpose_x_0"), val = bool(false)]; + bool x_95_transpose_y_0 = const()[name = string("x_95_transpose_y_0"), val = bool(false)]; + tensor var_925_to_fp16 = const()[name = string("op_925_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53702144)))]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_923_cast_fp16)[name = string("transpose_284")]; + tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_925_to_fp16)[name = string("x_95_cast_fp16")]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_97_mode_0 = const()[name = string("x_97_mode_0"), val = string("constant")]; + fp16 const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = fp16(0x0p+0)]; + tensor x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_933 = const()[name = string("op_933"), val = tensor([1, 8, -1, 188])]; + tensor x_99_cast_fp16 = reshape(shape = var_933, x = x_97_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor var_937_begin_0 = const()[name = string("op_937_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_937_end_0 = const()[name = string("op_937_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_937_end_mask_0 = const()[name = string("op_937_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_937_cast_fp16 = slice_by_index(begin = var_937_begin_0, end = var_937_end_0, end_mask = var_937_end_mask_0, x = x_99_cast_fp16)[name = string("op_937_cast_fp16")]; + tensor var_938 = const()[name = string("op_938"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_938, x = var_937_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_921_cast_fp16)[name = string("transpose_283")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_947_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_947_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_947_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = string("scores_19_cast_fp16")]; + tensor var_953_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = string("op_953_cast_fp16")]; + tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_953_cast_fp16, cond = mask_3)[name = string("input_241_cast_fp16")]; + bool x_101_transpose_x_0 = const()[name = string("x_101_transpose_x_0"), val = bool(false)]; + bool x_101_transpose_y_0 = const()[name = string("x_101_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")]; + tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_957_perm_0 = const()[name = string("op_957_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_958 = const()[name = string("op_958"), val = tensor([1, -1, 1024])]; + tensor var_957_cast_fp16 = transpose(perm = var_957_perm_0, x = x_101_cast_fp16)[name = string("transpose_280")]; + tensor input_243_cast_fp16 = reshape(shape = var_958, x = var_957_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54470208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54863488))))[name = string("module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_243_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = string("input_247_cast_fp16")]; + tensor x_105_axes_0 = const()[name = string("x_105_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54879936)))]; + tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54882048)))]; + tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor input_249_perm_0 = const()[name = string("input_249_perm_0"), val = tensor([0, 2, 1])]; + string input_251_pad_type_0 = const()[name = string("input_251_pad_type_0"), val = string("valid")]; + tensor input_251_strides_0 = const()[name = string("input_251_strides_0"), val = tensor([1])]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0])]; + tensor input_251_dilations_0 = const()[name = string("input_251_dilations_0"), val = tensor([1])]; + int32 input_251_groups_0 = const()[name = string("input_251_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54884160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55670656))))[name = string("module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = string("transpose_279")]; + tensor input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + int32 x_107_split_num_splits_0 = const()[name = string("x_107_split_num_splits_0"), val = int32(2)]; + int32 x_107_split_axis_0 = const()[name = string("x_107_split_axis_0"), val = int32(1)]; + tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = string("x_107_split_cast_fp16")]; + tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = string("x_107_split_1_sigmoid_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_328)[name = string("input_253_cast_fp16")]; + tensor input_255_pad_0 = const()[name = string("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("constant")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = string("input_255_cast_fp16")]; + string input_257_pad_type_0 = const()[name = string("input_257_pad_type_0"), val = string("valid")]; + int32 input_257_groups_0 = const()[name = string("input_257_groups_0"), val = int32(1024)]; + tensor input_257_strides_0 = const()[name = string("input_257_strides_0"), val = tensor([1])]; + tensor input_257_pad_0 = const()[name = string("input_257_pad_0"), val = tensor([0, 0])]; + tensor input_257_dilations_0 = const()[name = string("input_257_dilations_0"), val = tensor([1])]; + tensor const_256_to_fp16 = const()[name = string("const_256_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55703488)))]; + tensor const_257_to_fp16 = const()[name = string("const_257_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55721984)))]; + tensor input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16, x = input_255_cast_fp16)[name = string("input_259_cast_fp16")]; + tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = string("input_261_cast_fp16")]; + string x_109_pad_type_0 = const()[name = string("x_109_pad_type_0"), val = string("valid")]; + tensor x_109_strides_0 = const()[name = string("x_109_strides_0"), val = tensor([1])]; + tensor x_109_pad_0 = const()[name = string("x_109_pad_0"), val = tensor([0, 0])]; + tensor x_109_dilations_0 = const()[name = string("x_109_dilations_0"), val = tensor([1])]; + int32 x_109_groups_0 = const()[name = string("x_109_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55724096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56117376))))[name = string("module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_261_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = string("transpose_278")]; + tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor input_267_axes_0 = const()[name = string("input_267_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56133824)))]; + tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56135936)))]; + tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56138048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57710976))))[name = string("module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_267_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57776576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59349504))))[name = string("module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_271_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1019_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1019_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor input_279_axes_0 = const()[name = string("input_279_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = string("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59365952)))]; + tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = string("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59368064)))]; + tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = string("input_279_cast_fp16")]; + tensor input_281_axes_0 = const()[name = string("input_281_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59370176)))]; + tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59372288)))]; + tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = string("input_281_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59374400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60947328))))[name = string("module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_281_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61012928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62585856))))[name = string("module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_285_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1047_to_fp16 = const()[name = string("op_1047_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1048_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1047_to_fp16)[name = string("op_1048_cast_fp16")]; + tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1048_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62602304)))]; + tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62604416)))]; + tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = string("query_11_cast_fp16")]; + tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62606528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62999808))))[name = string("module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1064 = const()[name = string("op_1064"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1064, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63016256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63409536))))[name = string("module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1068 = const()[name = string("op_1068"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1068, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63425984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63819264))))[name = string("module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1072 = const()[name = string("op_1072"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1072, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63835712)))]; + tensor var_1084_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1084_cast_fp16")]; + tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63837824)))]; + tensor var_1086_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1086_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63839936)))]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1086_cast_fp16)[name = string("transpose_277")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1088_to_fp16)[name = string("x_117_cast_fp16")]; + tensor x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor var_1096 = const()[name = string("op_1096"), val = tensor([1, 8, -1, 188])]; + tensor x_121_cast_fp16 = reshape(shape = var_1096, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor var_1100_begin_0 = const()[name = string("op_1100_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1100_end_0 = const()[name = string("op_1100_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1100_end_mask_0 = const()[name = string("op_1100_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1100_cast_fp16 = slice_by_index(begin = var_1100_begin_0, end = var_1100_end_0, end_mask = var_1100_end_mask_0, x = x_121_cast_fp16)[name = string("op_1100_cast_fp16")]; + tensor var_1101 = const()[name = string("op_1101"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1101, x = var_1100_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1084_cast_fp16)[name = string("transpose_276")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1110_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1110_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1110_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = string("scores_23_cast_fp16")]; + tensor var_1116_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = string("op_1116_cast_fp16")]; + tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1116_cast_fp16, cond = mask_3)[name = string("input_293_cast_fp16")]; + bool x_123_transpose_x_0 = const()[name = string("x_123_transpose_x_0"), val = bool(false)]; + bool x_123_transpose_y_0 = const()[name = string("x_123_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")]; + tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1120_perm_0 = const()[name = string("op_1120_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1121 = const()[name = string("op_1121"), val = tensor([1, -1, 1024])]; + tensor var_1120_cast_fp16 = transpose(perm = var_1120_perm_0, x = x_123_cast_fp16)[name = string("transpose_273")]; + tensor input_295_cast_fp16 = reshape(shape = var_1121, x = var_1120_cast_fp16)[name = string("input_295_cast_fp16")]; + tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64608000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65001280))))[name = string("module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_295_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = string("input_299_cast_fp16")]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65017728)))]; + tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65019840)))]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_301_perm_0 = const()[name = string("input_301_perm_0"), val = tensor([0, 2, 1])]; + string input_303_pad_type_0 = const()[name = string("input_303_pad_type_0"), val = string("valid")]; + tensor input_303_strides_0 = const()[name = string("input_303_strides_0"), val = tensor([1])]; + tensor input_303_pad_0 = const()[name = string("input_303_pad_0"), val = tensor([0, 0])]; + tensor input_303_dilations_0 = const()[name = string("input_303_dilations_0"), val = tensor([1])]; + int32 input_303_groups_0 = const()[name = string("input_303_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65021952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65808448))))[name = string("module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = string("transpose_272")]; + tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_301_cast_fp16)[name = string("input_303_cast_fp16")]; + int32 x_129_split_num_splits_0 = const()[name = string("x_129_split_num_splits_0"), val = int32(2)]; + int32 x_129_split_axis_0 = const()[name = string("x_129_split_axis_0"), val = int32(1)]; + tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = string("x_129_split_cast_fp16")]; + tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = string("x_129_split_1_sigmoid_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_328)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("constant")]; + fp16 const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string input_309_pad_type_0 = const()[name = string("input_309_pad_type_0"), val = string("valid")]; + int32 input_309_groups_0 = const()[name = string("input_309_groups_0"), val = int32(1024)]; + tensor input_309_strides_0 = const()[name = string("input_309_strides_0"), val = tensor([1])]; + tensor input_309_pad_0 = const()[name = string("input_309_pad_0"), val = tensor([0, 0])]; + tensor input_309_dilations_0 = const()[name = string("input_309_dilations_0"), val = tensor([1])]; + tensor const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65841280)))]; + tensor const_259_to_fp16 = const()[name = string("const_259_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65859776)))]; + tensor input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16, x = input_307_cast_fp16)[name = string("input_311_cast_fp16")]; + tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = string("input_313_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("valid")]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([0, 0])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65861888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66255168))))[name = string("module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_313_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = string("transpose_271")]; + tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_axes_0 = const()[name = string("input_319_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66271616)))]; + tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66273728)))]; + tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66275840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67848768))))[name = string("module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_319_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67914368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69487296))))[name = string("module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_323_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1182_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1181_to_fp16)[name = string("op_1182_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1182_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_axes_0 = const()[name = string("input_331_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = string("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69503744)))]; + tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = string("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69505856)))]; + tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + tensor input_333_axes_0 = const()[name = string("input_333_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69507968)))]; + tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69510080)))]; + tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = string("input_333_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69512192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71085120))))[name = string("module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_333_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71150720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72723648))))[name = string("module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_337_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1210_to_fp16 = const()[name = string("op_1210_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1211_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1210_to_fp16)[name = string("op_1211_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1211_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72740096)))]; + tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72742208)))]; + tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = string("query_13_cast_fp16")]; + tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72744320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73137600))))[name = string("module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1227 = const()[name = string("op_1227"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1227, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73154048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73547328))))[name = string("module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1231 = const()[name = string("op_1231"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1231, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73563776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73957056))))[name = string("module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1235 = const()[name = string("op_1235"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1235, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73973504)))]; + tensor var_1247_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1247_cast_fp16")]; + tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73975616)))]; + tensor var_1249_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1249_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_139_transpose_x_0 = const()[name = string("x_139_transpose_x_0"), val = bool(false)]; + bool x_139_transpose_y_0 = const()[name = string("x_139_transpose_y_0"), val = bool(false)]; + tensor var_1251_to_fp16 = const()[name = string("op_1251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73977728)))]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1249_cast_fp16)[name = string("transpose_270")]; + tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1251_to_fp16)[name = string("x_139_cast_fp16")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_141_mode_0 = const()[name = string("x_141_mode_0"), val = string("constant")]; + fp16 const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = fp16(0x0p+0)]; + tensor x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1259 = const()[name = string("op_1259"), val = tensor([1, 8, -1, 188])]; + tensor x_143_cast_fp16 = reshape(shape = var_1259, x = x_141_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1263_begin_0 = const()[name = string("op_1263_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1263_end_0 = const()[name = string("op_1263_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1263_end_mask_0 = const()[name = string("op_1263_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1263_cast_fp16 = slice_by_index(begin = var_1263_begin_0, end = var_1263_end_0, end_mask = var_1263_end_mask_0, x = x_143_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor var_1264 = const()[name = string("op_1264"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1264, x = var_1263_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1247_cast_fp16)[name = string("transpose_269")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1273_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1273_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1273_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = string("scores_27_cast_fp16")]; + tensor var_1279_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = string("op_1279_cast_fp16")]; + tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1279_cast_fp16, cond = mask_3)[name = string("input_345_cast_fp16")]; + bool x_145_transpose_x_0 = const()[name = string("x_145_transpose_x_0"), val = bool(false)]; + bool x_145_transpose_y_0 = const()[name = string("x_145_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")]; + tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1283_perm_0 = const()[name = string("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1284 = const()[name = string("op_1284"), val = tensor([1, -1, 1024])]; + tensor var_1283_cast_fp16 = transpose(perm = var_1283_perm_0, x = x_145_cast_fp16)[name = string("transpose_266")]; + tensor input_347_cast_fp16 = reshape(shape = var_1284, x = var_1283_cast_fp16)[name = string("input_347_cast_fp16")]; + tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74745792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75139072))))[name = string("module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_347_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = string("input_351_cast_fp16")]; + tensor x_149_axes_0 = const()[name = string("x_149_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75155520)))]; + tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75157632)))]; + tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + string input_355_pad_type_0 = const()[name = string("input_355_pad_type_0"), val = string("valid")]; + tensor input_355_strides_0 = const()[name = string("input_355_strides_0"), val = tensor([1])]; + tensor input_355_pad_0 = const()[name = string("input_355_pad_0"), val = tensor([0, 0])]; + tensor input_355_dilations_0 = const()[name = string("input_355_dilations_0"), val = tensor([1])]; + int32 input_355_groups_0 = const()[name = string("input_355_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75159744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75946240))))[name = string("module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = string("transpose_265")]; + tensor input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_353_cast_fp16)[name = string("input_355_cast_fp16")]; + int32 x_151_split_num_splits_0 = const()[name = string("x_151_split_num_splits_0"), val = int32(2)]; + int32 x_151_split_axis_0 = const()[name = string("x_151_split_axis_0"), val = int32(1)]; + tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = string("x_151_split_cast_fp16")]; + tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = string("x_151_split_1_sigmoid_cast_fp16")]; + tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_328)[name = string("input_357_cast_fp16")]; + tensor input_359_pad_0 = const()[name = string("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_359_mode_0 = const()[name = string("input_359_mode_0"), val = string("constant")]; + fp16 const_77_to_fp16 = const()[name = string("const_77_to_fp16"), val = fp16(0x0p+0)]; + tensor input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = string("input_359_cast_fp16")]; + string input_361_pad_type_0 = const()[name = string("input_361_pad_type_0"), val = string("valid")]; + int32 input_361_groups_0 = const()[name = string("input_361_groups_0"), val = int32(1024)]; + tensor input_361_strides_0 = const()[name = string("input_361_strides_0"), val = tensor([1])]; + tensor input_361_pad_0 = const()[name = string("input_361_pad_0"), val = tensor([0, 0])]; + tensor input_361_dilations_0 = const()[name = string("input_361_dilations_0"), val = tensor([1])]; + tensor const_260_to_fp16 = const()[name = string("const_260_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75979072)))]; + tensor const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75997568)))]; + tensor input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16, x = input_359_cast_fp16)[name = string("input_363_cast_fp16")]; + tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = string("input_365_cast_fp16")]; + string x_153_pad_type_0 = const()[name = string("x_153_pad_type_0"), val = string("valid")]; + tensor x_153_strides_0 = const()[name = string("x_153_strides_0"), val = tensor([1])]; + tensor x_153_pad_0 = const()[name = string("x_153_pad_0"), val = tensor([0, 0])]; + tensor x_153_dilations_0 = const()[name = string("x_153_dilations_0"), val = tensor([1])]; + int32 x_153_groups_0 = const()[name = string("x_153_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75999680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76392960))))[name = string("module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_365_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = string("transpose_264")]; + tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + tensor input_371_axes_0 = const()[name = string("input_371_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76409408)))]; + tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76411520)))]; + tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76413632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77986560))))[name = string("module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_371_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78052160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79625088))))[name = string("module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_375_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1345_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1345_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor input_383_axes_0 = const()[name = string("input_383_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = string("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79641536)))]; + tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = string("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79643648)))]; + tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = string("input_383_cast_fp16")]; + tensor input_385_axes_0 = const()[name = string("input_385_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79645760)))]; + tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79647872)))]; + tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = string("input_385_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79649984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81222912))))[name = string("module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_385_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81288512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82861440))))[name = string("module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_389_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1373_to_fp16 = const()[name = string("op_1373_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1374_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1373_to_fp16)[name = string("op_1374_cast_fp16")]; + tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1374_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82877888)))]; + tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82880000)))]; + tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = string("query_15_cast_fp16")]; + tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82882112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83275392))))[name = string("module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1390 = const()[name = string("op_1390"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1390, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83291840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83685120))))[name = string("module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1394 = const()[name = string("op_1394"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1394, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83701568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84094848))))[name = string("module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1398 = const()[name = string("op_1398"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1398, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84111296)))]; + tensor var_1410_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1410_cast_fp16")]; + tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84113408)))]; + tensor var_1412_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1412_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_161_transpose_x_0 = const()[name = string("x_161_transpose_x_0"), val = bool(false)]; + bool x_161_transpose_y_0 = const()[name = string("x_161_transpose_y_0"), val = bool(false)]; + tensor var_1414_to_fp16 = const()[name = string("op_1414_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84115520)))]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1412_cast_fp16)[name = string("transpose_263")]; + tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1414_to_fp16)[name = string("x_161_cast_fp16")]; + tensor x_163_pad_0 = const()[name = string("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_163_mode_0 = const()[name = string("x_163_mode_0"), val = string("constant")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor var_1422 = const()[name = string("op_1422"), val = tensor([1, 8, -1, 188])]; + tensor x_165_cast_fp16 = reshape(shape = var_1422, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1426_begin_0 = const()[name = string("op_1426_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1426_end_0 = const()[name = string("op_1426_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1426_end_mask_0 = const()[name = string("op_1426_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = x_165_cast_fp16)[name = string("op_1426_cast_fp16")]; + tensor var_1427 = const()[name = string("op_1427"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1427, x = var_1426_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1410_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_1436_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1436_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_1436_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = string("scores_31_cast_fp16")]; + tensor var_1442_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = string("op_1442_cast_fp16")]; + tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1442_cast_fp16, cond = mask_3)[name = string("input_397_cast_fp16")]; + bool x_167_transpose_x_0 = const()[name = string("x_167_transpose_x_0"), val = bool(false)]; + bool x_167_transpose_y_0 = const()[name = string("x_167_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")]; + tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1446_perm_0 = const()[name = string("op_1446_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1447 = const()[name = string("op_1447"), val = tensor([1, -1, 1024])]; + tensor var_1446_cast_fp16 = transpose(perm = var_1446_perm_0, x = x_167_cast_fp16)[name = string("transpose_259")]; + tensor input_399_cast_fp16 = reshape(shape = var_1447, x = var_1446_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84883584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85276864))))[name = string("module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_399_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = string("input_403_cast_fp16")]; + tensor x_171_axes_0 = const()[name = string("x_171_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85293312)))]; + tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85295424)))]; + tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string input_407_pad_type_0 = const()[name = string("input_407_pad_type_0"), val = string("valid")]; + tensor input_407_strides_0 = const()[name = string("input_407_strides_0"), val = tensor([1])]; + tensor input_407_pad_0 = const()[name = string("input_407_pad_0"), val = tensor([0, 0])]; + tensor input_407_dilations_0 = const()[name = string("input_407_dilations_0"), val = tensor([1])]; + int32 input_407_groups_0 = const()[name = string("input_407_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85297536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084032))))[name = string("module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = string("transpose_258")]; + tensor input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_405_cast_fp16)[name = string("input_407_cast_fp16")]; + int32 x_173_split_num_splits_0 = const()[name = string("x_173_split_num_splits_0"), val = int32(2)]; + int32 x_173_split_axis_0 = const()[name = string("x_173_split_axis_0"), val = int32(1)]; + tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = string("x_173_split_cast_fp16")]; + tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = string("x_173_split_1_sigmoid_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_328)[name = string("input_409_cast_fp16")]; + tensor input_411_pad_0 = const()[name = string("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_411_mode_0 = const()[name = string("input_411_mode_0"), val = string("constant")]; + fp16 const_87_to_fp16 = const()[name = string("const_87_to_fp16"), val = fp16(0x0p+0)]; + tensor input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = string("input_411_cast_fp16")]; + string input_413_pad_type_0 = const()[name = string("input_413_pad_type_0"), val = string("valid")]; + int32 input_413_groups_0 = const()[name = string("input_413_groups_0"), val = int32(1024)]; + tensor input_413_strides_0 = const()[name = string("input_413_strides_0"), val = tensor([1])]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0])]; + tensor input_413_dilations_0 = const()[name = string("input_413_dilations_0"), val = tensor([1])]; + tensor const_262_to_fp16 = const()[name = string("const_262_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86116864)))]; + tensor const_263_to_fp16 = const()[name = string("const_263_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86135360)))]; + tensor input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16, x = input_411_cast_fp16)[name = string("input_415_cast_fp16")]; + tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = string("input_417_cast_fp16")]; + string x_175_pad_type_0 = const()[name = string("x_175_pad_type_0"), val = string("valid")]; + tensor x_175_strides_0 = const()[name = string("x_175_strides_0"), val = tensor([1])]; + tensor x_175_pad_0 = const()[name = string("x_175_pad_0"), val = tensor([0, 0])]; + tensor x_175_dilations_0 = const()[name = string("x_175_dilations_0"), val = tensor([1])]; + int32 x_175_groups_0 = const()[name = string("x_175_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86137472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86530752))))[name = string("module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_417_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = string("transpose_257")]; + tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = string("input_421_cast_fp16")]; + tensor input_423_axes_0 = const()[name = string("input_423_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86547200)))]; + tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86549312)))]; + tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86551424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88124352))))[name = string("module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_423_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88189952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89762880))))[name = string("module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_427_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1508_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1508_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor input_435_axes_0 = const()[name = string("input_435_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = string("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89779328)))]; + tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = string("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89781440)))]; + tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = string("input_435_cast_fp16")]; + tensor input_437_axes_0 = const()[name = string("input_437_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89783552)))]; + tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89785664)))]; + tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = string("input_437_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89787776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91360704))))[name = string("module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_437_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91426304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92999232))))[name = string("module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_441_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_1536_to_fp16 = const()[name = string("op_1536_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1537_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1536_to_fp16)[name = string("op_1537_cast_fp16")]; + tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1537_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93015680)))]; + tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93017792)))]; + tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = string("query_17_cast_fp16")]; + tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93019904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93413184))))[name = string("module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_1553 = const()[name = string("op_1553"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_1553, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93429632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93822912))))[name = string("module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_1557 = const()[name = string("op_1557"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_1557, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93839360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94232640))))[name = string("module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_1561 = const()[name = string("op_1561"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_1561, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94249088)))]; + tensor var_1573_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1573_cast_fp16")]; + tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94251200)))]; + tensor var_1575_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1575_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_183_transpose_x_0 = const()[name = string("x_183_transpose_x_0"), val = bool(false)]; + bool x_183_transpose_y_0 = const()[name = string("x_183_transpose_y_0"), val = bool(false)]; + tensor var_1577_to_fp16 = const()[name = string("op_1577_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94253312)))]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1575_cast_fp16)[name = string("transpose_256")]; + tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1577_to_fp16)[name = string("x_183_cast_fp16")]; + tensor x_185_pad_0 = const()[name = string("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_185_mode_0 = const()[name = string("x_185_mode_0"), val = string("constant")]; + fp16 const_94_to_fp16 = const()[name = string("const_94_to_fp16"), val = fp16(0x0p+0)]; + tensor x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor var_1585 = const()[name = string("op_1585"), val = tensor([1, 8, -1, 188])]; + tensor x_187_cast_fp16 = reshape(shape = var_1585, x = x_185_cast_fp16)[name = string("x_187_cast_fp16")]; + tensor var_1589_begin_0 = const()[name = string("op_1589_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1589_end_0 = const()[name = string("op_1589_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1589_end_mask_0 = const()[name = string("op_1589_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1589_cast_fp16 = slice_by_index(begin = var_1589_begin_0, end = var_1589_end_0, end_mask = var_1589_end_mask_0, x = x_187_cast_fp16)[name = string("op_1589_cast_fp16")]; + tensor var_1590 = const()[name = string("op_1590"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1590, x = var_1589_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1573_cast_fp16)[name = string("transpose_255")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_1599_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1599_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_1599_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = string("scores_35_cast_fp16")]; + tensor var_1605_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = string("op_1605_cast_fp16")]; + tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1605_cast_fp16, cond = mask_3)[name = string("input_449_cast_fp16")]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = string("x_189_cast_fp16")]; + tensor var_1609_perm_0 = const()[name = string("op_1609_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1610 = const()[name = string("op_1610"), val = tensor([1, -1, 1024])]; + tensor var_1609_cast_fp16 = transpose(perm = var_1609_perm_0, x = x_189_cast_fp16)[name = string("transpose_252")]; + tensor input_451_cast_fp16 = reshape(shape = var_1610, x = var_1609_cast_fp16)[name = string("input_451_cast_fp16")]; + tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95021376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95414656))))[name = string("module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_451_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = string("input_455_cast_fp16")]; + tensor x_193_axes_0 = const()[name = string("x_193_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95431104)))]; + tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95433216)))]; + tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor input_457_perm_0 = const()[name = string("input_457_perm_0"), val = tensor([0, 2, 1])]; + string input_459_pad_type_0 = const()[name = string("input_459_pad_type_0"), val = string("valid")]; + tensor input_459_strides_0 = const()[name = string("input_459_strides_0"), val = tensor([1])]; + tensor input_459_pad_0 = const()[name = string("input_459_pad_0"), val = tensor([0, 0])]; + tensor input_459_dilations_0 = const()[name = string("input_459_dilations_0"), val = tensor([1])]; + int32 input_459_groups_0 = const()[name = string("input_459_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95435328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96221824))))[name = string("module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = string("transpose_251")]; + tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_457_cast_fp16)[name = string("input_459_cast_fp16")]; + int32 x_195_split_num_splits_0 = const()[name = string("x_195_split_num_splits_0"), val = int32(2)]; + int32 x_195_split_axis_0 = const()[name = string("x_195_split_axis_0"), val = int32(1)]; + tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = string("x_195_split_cast_fp16")]; + tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = string("x_195_split_1_sigmoid_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_328)[name = string("input_461_cast_fp16")]; + tensor input_463_pad_0 = const()[name = string("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_463_mode_0 = const()[name = string("input_463_mode_0"), val = string("constant")]; + fp16 const_97_to_fp16 = const()[name = string("const_97_to_fp16"), val = fp16(0x0p+0)]; + tensor input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = string("input_463_cast_fp16")]; + string input_465_pad_type_0 = const()[name = string("input_465_pad_type_0"), val = string("valid")]; + int32 input_465_groups_0 = const()[name = string("input_465_groups_0"), val = int32(1024)]; + tensor input_465_strides_0 = const()[name = string("input_465_strides_0"), val = tensor([1])]; + tensor input_465_pad_0 = const()[name = string("input_465_pad_0"), val = tensor([0, 0])]; + tensor input_465_dilations_0 = const()[name = string("input_465_dilations_0"), val = tensor([1])]; + tensor const_264_to_fp16 = const()[name = string("const_264_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96254656)))]; + tensor const_265_to_fp16 = const()[name = string("const_265_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96273152)))]; + tensor input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16, x = input_463_cast_fp16)[name = string("input_467_cast_fp16")]; + tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = string("input_469_cast_fp16")]; + string x_197_pad_type_0 = const()[name = string("x_197_pad_type_0"), val = string("valid")]; + tensor x_197_strides_0 = const()[name = string("x_197_strides_0"), val = tensor([1])]; + tensor x_197_pad_0 = const()[name = string("x_197_pad_0"), val = tensor([0, 0])]; + tensor x_197_dilations_0 = const()[name = string("x_197_dilations_0"), val = tensor([1])]; + int32 x_197_groups_0 = const()[name = string("x_197_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96275264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96668544))))[name = string("module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_469_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = string("transpose_250")]; + tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = string("input_473_cast_fp16")]; + tensor input_475_axes_0 = const()[name = string("input_475_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96684992)))]; + tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96687104)))]; + tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96689216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98262144))))[name = string("module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_475_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98327744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99900672))))[name = string("module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_479_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_1670_to_fp16 = const()[name = string("op_1670_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1671_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1670_to_fp16)[name = string("op_1671_cast_fp16")]; + tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1671_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor input_487_axes_0 = const()[name = string("input_487_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = string("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99917120)))]; + tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = string("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99919232)))]; + tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = string("input_487_cast_fp16")]; + tensor input_489_axes_0 = const()[name = string("input_489_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99921344)))]; + tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99923456)))]; + tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = string("input_489_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99925568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101498496))))[name = string("module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_489_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101564096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103137024))))[name = string("module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_493_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_1699_to_fp16 = const()[name = string("op_1699_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1700_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1699_to_fp16)[name = string("op_1700_cast_fp16")]; + tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1700_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103153472)))]; + tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103155584)))]; + tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = string("query_19_cast_fp16")]; + tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103157696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103550976))))[name = string("module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_1716 = const()[name = string("op_1716"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_1716, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103567424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103960704))))[name = string("module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_1720 = const()[name = string("op_1720"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_1720, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103977152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104370432))))[name = string("module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_1724 = const()[name = string("op_1724"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_1724, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104386880)))]; + tensor var_1736_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_1736_cast_fp16")]; + tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104388992)))]; + tensor var_1738_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_1738_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_205_transpose_x_0 = const()[name = string("x_205_transpose_x_0"), val = bool(false)]; + bool x_205_transpose_y_0 = const()[name = string("x_205_transpose_y_0"), val = bool(false)]; + tensor var_1740_to_fp16 = const()[name = string("op_1740_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104391104)))]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1738_cast_fp16)[name = string("transpose_249")]; + tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1740_to_fp16)[name = string("x_205_cast_fp16")]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_207_mode_0 = const()[name = string("x_207_mode_0"), val = string("constant")]; + fp16 const_104_to_fp16 = const()[name = string("const_104_to_fp16"), val = fp16(0x0p+0)]; + tensor x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor var_1748 = const()[name = string("op_1748"), val = tensor([1, 8, -1, 188])]; + tensor x_209_cast_fp16 = reshape(shape = var_1748, x = x_207_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor var_1752_begin_0 = const()[name = string("op_1752_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1752_end_0 = const()[name = string("op_1752_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1752_end_mask_0 = const()[name = string("op_1752_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1752_cast_fp16 = slice_by_index(begin = var_1752_begin_0, end = var_1752_end_0, end_mask = var_1752_end_mask_0, x = x_209_cast_fp16)[name = string("op_1752_cast_fp16")]; + tensor var_1753 = const()[name = string("op_1753"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1753, x = var_1752_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1736_cast_fp16)[name = string("transpose_248")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_1762_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_1762_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_1762_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = string("scores_39_cast_fp16")]; + tensor var_1768_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = string("op_1768_cast_fp16")]; + tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1768_cast_fp16, cond = mask_3)[name = string("input_501_cast_fp16")]; + bool x_211_transpose_x_0 = const()[name = string("x_211_transpose_x_0"), val = bool(false)]; + bool x_211_transpose_y_0 = const()[name = string("x_211_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")]; + tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_1772_perm_0 = const()[name = string("op_1772_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1773 = const()[name = string("op_1773"), val = tensor([1, -1, 1024])]; + tensor var_1772_cast_fp16 = transpose(perm = var_1772_perm_0, x = x_211_cast_fp16)[name = string("transpose_245")]; + tensor input_503_cast_fp16 = reshape(shape = var_1773, x = var_1772_cast_fp16)[name = string("input_503_cast_fp16")]; + tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105159168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105552448))))[name = string("module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_503_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = string("input_507_cast_fp16")]; + tensor x_215_axes_0 = const()[name = string("x_215_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105568896)))]; + tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105571008)))]; + tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor input_509_perm_0 = const()[name = string("input_509_perm_0"), val = tensor([0, 2, 1])]; + string input_511_pad_type_0 = const()[name = string("input_511_pad_type_0"), val = string("valid")]; + tensor input_511_strides_0 = const()[name = string("input_511_strides_0"), val = tensor([1])]; + tensor input_511_pad_0 = const()[name = string("input_511_pad_0"), val = tensor([0, 0])]; + tensor input_511_dilations_0 = const()[name = string("input_511_dilations_0"), val = tensor([1])]; + int32 input_511_groups_0 = const()[name = string("input_511_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105573120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106359616))))[name = string("module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = string("transpose_244")]; + tensor input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_509_cast_fp16)[name = string("input_511_cast_fp16")]; + int32 x_217_split_num_splits_0 = const()[name = string("x_217_split_num_splits_0"), val = int32(2)]; + int32 x_217_split_axis_0 = const()[name = string("x_217_split_axis_0"), val = int32(1)]; + tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = string("x_217_split_cast_fp16")]; + tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = string("x_217_split_1_sigmoid_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_328)[name = string("input_513_cast_fp16")]; + tensor input_515_pad_0 = const()[name = string("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_515_mode_0 = const()[name = string("input_515_mode_0"), val = string("constant")]; + fp16 const_107_to_fp16 = const()[name = string("const_107_to_fp16"), val = fp16(0x0p+0)]; + tensor input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = string("input_515_cast_fp16")]; + string input_517_pad_type_0 = const()[name = string("input_517_pad_type_0"), val = string("valid")]; + int32 input_517_groups_0 = const()[name = string("input_517_groups_0"), val = int32(1024)]; + tensor input_517_strides_0 = const()[name = string("input_517_strides_0"), val = tensor([1])]; + tensor input_517_pad_0 = const()[name = string("input_517_pad_0"), val = tensor([0, 0])]; + tensor input_517_dilations_0 = const()[name = string("input_517_dilations_0"), val = tensor([1])]; + tensor const_266_to_fp16 = const()[name = string("const_266_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106392448)))]; + tensor const_267_to_fp16 = const()[name = string("const_267_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106410944)))]; + tensor input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16, x = input_515_cast_fp16)[name = string("input_519_cast_fp16")]; + tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = string("input_521_cast_fp16")]; + string x_219_pad_type_0 = const()[name = string("x_219_pad_type_0"), val = string("valid")]; + tensor x_219_strides_0 = const()[name = string("x_219_strides_0"), val = tensor([1])]; + tensor x_219_pad_0 = const()[name = string("x_219_pad_0"), val = tensor([0, 0])]; + tensor x_219_dilations_0 = const()[name = string("x_219_dilations_0"), val = tensor([1])]; + int32 x_219_groups_0 = const()[name = string("x_219_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106413056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106806336))))[name = string("module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_521_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = string("transpose_243")]; + tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = string("input_525_cast_fp16")]; + tensor input_527_axes_0 = const()[name = string("input_527_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106822784)))]; + tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106824896)))]; + tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106827008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108399936))))[name = string("module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_527_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108465536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110038464))))[name = string("module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_531_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_1833_to_fp16 = const()[name = string("op_1833_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1834_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1833_to_fp16)[name = string("op_1834_cast_fp16")]; + tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1834_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor input_539_axes_0 = const()[name = string("input_539_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = string("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110054912)))]; + tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = string("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110057024)))]; + tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = string("input_539_cast_fp16")]; + tensor input_541_axes_0 = const()[name = string("input_541_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110059136)))]; + tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110061248)))]; + tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = string("input_541_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110063360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111636288))))[name = string("module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_541_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111701888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113274816))))[name = string("module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_545_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_1862_to_fp16 = const()[name = string("op_1862_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1863_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1862_to_fp16)[name = string("op_1863_cast_fp16")]; + tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1863_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113291264)))]; + tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113293376)))]; + tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = string("query_21_cast_fp16")]; + tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113295488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113688768))))[name = string("module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_1879 = const()[name = string("op_1879"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_1879, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113705216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114098496))))[name = string("module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_1883 = const()[name = string("op_1883"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_1883, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114114944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114508224))))[name = string("module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_1887 = const()[name = string("op_1887"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_1887, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114524672)))]; + tensor var_1899_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_1899_cast_fp16")]; + tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114526784)))]; + tensor var_1901_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_1901_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_227_transpose_x_0 = const()[name = string("x_227_transpose_x_0"), val = bool(false)]; + bool x_227_transpose_y_0 = const()[name = string("x_227_transpose_y_0"), val = bool(false)]; + tensor var_1903_to_fp16 = const()[name = string("op_1903_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114528896)))]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1901_cast_fp16)[name = string("transpose_242")]; + tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_1903_to_fp16)[name = string("x_227_cast_fp16")]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_229_mode_0 = const()[name = string("x_229_mode_0"), val = string("constant")]; + fp16 const_114_to_fp16 = const()[name = string("const_114_to_fp16"), val = fp16(0x0p+0)]; + tensor x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor var_1911 = const()[name = string("op_1911"), val = tensor([1, 8, -1, 188])]; + tensor x_231_cast_fp16 = reshape(shape = var_1911, x = x_229_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor var_1915_begin_0 = const()[name = string("op_1915_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1915_end_0 = const()[name = string("op_1915_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1915_end_mask_0 = const()[name = string("op_1915_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1915_cast_fp16 = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = x_231_cast_fp16)[name = string("op_1915_cast_fp16")]; + tensor var_1916 = const()[name = string("op_1916"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_1916, x = var_1915_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1899_cast_fp16)[name = string("transpose_241")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_1925_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_1925_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_1925_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = string("scores_43_cast_fp16")]; + tensor var_1931_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = string("op_1931_cast_fp16")]; + tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_1931_cast_fp16, cond = mask_3)[name = string("input_553_cast_fp16")]; + bool x_233_transpose_x_0 = const()[name = string("x_233_transpose_x_0"), val = bool(false)]; + bool x_233_transpose_y_0 = const()[name = string("x_233_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")]; + tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor var_1935_perm_0 = const()[name = string("op_1935_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1936 = const()[name = string("op_1936"), val = tensor([1, -1, 1024])]; + tensor var_1935_cast_fp16 = transpose(perm = var_1935_perm_0, x = x_233_cast_fp16)[name = string("transpose_238")]; + tensor input_555_cast_fp16 = reshape(shape = var_1936, x = var_1935_cast_fp16)[name = string("input_555_cast_fp16")]; + tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115296960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115690240))))[name = string("module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_555_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = string("input_559_cast_fp16")]; + tensor x_237_axes_0 = const()[name = string("x_237_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115706688)))]; + tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115708800)))]; + tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = string("x_237_cast_fp16")]; + tensor input_561_perm_0 = const()[name = string("input_561_perm_0"), val = tensor([0, 2, 1])]; + string input_563_pad_type_0 = const()[name = string("input_563_pad_type_0"), val = string("valid")]; + tensor input_563_strides_0 = const()[name = string("input_563_strides_0"), val = tensor([1])]; + tensor input_563_pad_0 = const()[name = string("input_563_pad_0"), val = tensor([0, 0])]; + tensor input_563_dilations_0 = const()[name = string("input_563_dilations_0"), val = tensor([1])]; + int32 input_563_groups_0 = const()[name = string("input_563_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115710912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116497408))))[name = string("module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = string("transpose_237")]; + tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_561_cast_fp16)[name = string("input_563_cast_fp16")]; + int32 x_239_split_num_splits_0 = const()[name = string("x_239_split_num_splits_0"), val = int32(2)]; + int32 x_239_split_axis_0 = const()[name = string("x_239_split_axis_0"), val = int32(1)]; + tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = string("x_239_split_cast_fp16")]; + tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = string("x_239_split_1_sigmoid_cast_fp16")]; + tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = string("x_239_cast_fp16")]; + tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_328)[name = string("input_565_cast_fp16")]; + tensor input_567_pad_0 = const()[name = string("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_567_mode_0 = const()[name = string("input_567_mode_0"), val = string("constant")]; + fp16 const_117_to_fp16 = const()[name = string("const_117_to_fp16"), val = fp16(0x0p+0)]; + tensor input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = string("input_567_cast_fp16")]; + string input_569_pad_type_0 = const()[name = string("input_569_pad_type_0"), val = string("valid")]; + int32 input_569_groups_0 = const()[name = string("input_569_groups_0"), val = int32(1024)]; + tensor input_569_strides_0 = const()[name = string("input_569_strides_0"), val = tensor([1])]; + tensor input_569_pad_0 = const()[name = string("input_569_pad_0"), val = tensor([0, 0])]; + tensor input_569_dilations_0 = const()[name = string("input_569_dilations_0"), val = tensor([1])]; + tensor const_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116530240)))]; + tensor const_269_to_fp16 = const()[name = string("const_269_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116548736)))]; + tensor input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16, x = input_567_cast_fp16)[name = string("input_571_cast_fp16")]; + tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = string("input_573_cast_fp16")]; + string x_241_pad_type_0 = const()[name = string("x_241_pad_type_0"), val = string("valid")]; + tensor x_241_strides_0 = const()[name = string("x_241_strides_0"), val = tensor([1])]; + tensor x_241_pad_0 = const()[name = string("x_241_pad_0"), val = tensor([0, 0])]; + tensor x_241_dilations_0 = const()[name = string("x_241_dilations_0"), val = tensor([1])]; + int32 x_241_groups_0 = const()[name = string("x_241_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116550848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116944128))))[name = string("module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_573_cast_fp16)[name = string("x_241_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = string("transpose_236")]; + tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = string("input_577_cast_fp16")]; + tensor input_579_axes_0 = const()[name = string("input_579_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116960576)))]; + tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116962688)))]; + tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116964800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118537728))))[name = string("module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_579_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118603328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120176256))))[name = string("module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_583_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_1996_to_fp16 = const()[name = string("op_1996_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1997_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1996_to_fp16)[name = string("op_1997_cast_fp16")]; + tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1997_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor input_591_axes_0 = const()[name = string("input_591_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = string("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120192704)))]; + tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = string("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120194816)))]; + tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = string("input_591_cast_fp16")]; + tensor input_593_axes_0 = const()[name = string("input_593_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120196928)))]; + tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120199040)))]; + tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = string("input_593_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120201152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121774080))))[name = string("module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_593_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121839680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123412608))))[name = string("module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_597_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2025_to_fp16 = const()[name = string("op_2025_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2026_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2025_to_fp16)[name = string("op_2026_cast_fp16")]; + tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2026_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123429056)))]; + tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123431168)))]; + tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = string("query_23_cast_fp16")]; + tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123433280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123826560))))[name = string("module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2042 = const()[name = string("op_2042"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2042, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123843008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124236288))))[name = string("module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2046 = const()[name = string("op_2046"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2046, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124252736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124646016))))[name = string("module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2050 = const()[name = string("op_2050"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2050, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124662464)))]; + tensor var_2062_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2062_cast_fp16")]; + tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124664576)))]; + tensor var_2064_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2064_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_249_transpose_x_0 = const()[name = string("x_249_transpose_x_0"), val = bool(false)]; + bool x_249_transpose_y_0 = const()[name = string("x_249_transpose_y_0"), val = bool(false)]; + tensor var_2066_to_fp16 = const()[name = string("op_2066_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124666688)))]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2064_cast_fp16)[name = string("transpose_235")]; + tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2066_to_fp16)[name = string("x_249_cast_fp16")]; + tensor x_251_pad_0 = const()[name = string("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_251_mode_0 = const()[name = string("x_251_mode_0"), val = string("constant")]; + fp16 const_124_to_fp16 = const()[name = string("const_124_to_fp16"), val = fp16(0x0p+0)]; + tensor x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor var_2074 = const()[name = string("op_2074"), val = tensor([1, 8, -1, 188])]; + tensor x_253_cast_fp16 = reshape(shape = var_2074, x = x_251_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor var_2078_begin_0 = const()[name = string("op_2078_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2078_end_0 = const()[name = string("op_2078_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2078_end_mask_0 = const()[name = string("op_2078_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = x_253_cast_fp16)[name = string("op_2078_cast_fp16")]; + tensor var_2079 = const()[name = string("op_2079"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2079, x = var_2078_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2062_cast_fp16)[name = string("transpose_234")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2088_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2088_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2088_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = string("scores_47_cast_fp16")]; + tensor var_2094_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2094_cast_fp16, cond = mask_3)[name = string("input_605_cast_fp16")]; + bool x_255_transpose_x_0 = const()[name = string("x_255_transpose_x_0"), val = bool(false)]; + bool x_255_transpose_y_0 = const()[name = string("x_255_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")]; + tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2099 = const()[name = string("op_2099"), val = tensor([1, -1, 1024])]; + tensor var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = x_255_cast_fp16)[name = string("transpose_231")]; + tensor input_607_cast_fp16 = reshape(shape = var_2099, x = var_2098_cast_fp16)[name = string("input_607_cast_fp16")]; + tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125434752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125828032))))[name = string("module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_607_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = string("input_611_cast_fp16")]; + tensor x_259_axes_0 = const()[name = string("x_259_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125844480)))]; + tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125846592)))]; + tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_613_perm_0 = const()[name = string("input_613_perm_0"), val = tensor([0, 2, 1])]; + string input_615_pad_type_0 = const()[name = string("input_615_pad_type_0"), val = string("valid")]; + tensor input_615_strides_0 = const()[name = string("input_615_strides_0"), val = tensor([1])]; + tensor input_615_pad_0 = const()[name = string("input_615_pad_0"), val = tensor([0, 0])]; + tensor input_615_dilations_0 = const()[name = string("input_615_dilations_0"), val = tensor([1])]; + int32 input_615_groups_0 = const()[name = string("input_615_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125848704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126635200))))[name = string("module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = string("transpose_230")]; + tensor input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_613_cast_fp16)[name = string("input_615_cast_fp16")]; + int32 x_261_split_num_splits_0 = const()[name = string("x_261_split_num_splits_0"), val = int32(2)]; + int32 x_261_split_axis_0 = const()[name = string("x_261_split_axis_0"), val = int32(1)]; + tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = string("x_261_split_cast_fp16")]; + tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = string("x_261_split_1_sigmoid_cast_fp16")]; + tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = string("x_261_cast_fp16")]; + tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_328)[name = string("input_617_cast_fp16")]; + tensor input_619_pad_0 = const()[name = string("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_619_mode_0 = const()[name = string("input_619_mode_0"), val = string("constant")]; + fp16 const_127_to_fp16 = const()[name = string("const_127_to_fp16"), val = fp16(0x0p+0)]; + tensor input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = string("input_619_cast_fp16")]; + string input_621_pad_type_0 = const()[name = string("input_621_pad_type_0"), val = string("valid")]; + int32 input_621_groups_0 = const()[name = string("input_621_groups_0"), val = int32(1024)]; + tensor input_621_strides_0 = const()[name = string("input_621_strides_0"), val = tensor([1])]; + tensor input_621_pad_0 = const()[name = string("input_621_pad_0"), val = tensor([0, 0])]; + tensor input_621_dilations_0 = const()[name = string("input_621_dilations_0"), val = tensor([1])]; + tensor const_270_to_fp16 = const()[name = string("const_270_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126668032)))]; + tensor const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126686528)))]; + tensor input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16, x = input_619_cast_fp16)[name = string("input_623_cast_fp16")]; + tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = string("input_625_cast_fp16")]; + string x_263_pad_type_0 = const()[name = string("x_263_pad_type_0"), val = string("valid")]; + tensor x_263_strides_0 = const()[name = string("x_263_strides_0"), val = tensor([1])]; + tensor x_263_pad_0 = const()[name = string("x_263_pad_0"), val = tensor([0, 0])]; + tensor x_263_dilations_0 = const()[name = string("x_263_dilations_0"), val = tensor([1])]; + int32 x_263_groups_0 = const()[name = string("x_263_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126688640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127081920))))[name = string("module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_625_cast_fp16)[name = string("x_263_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = string("transpose_229")]; + tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = string("input_629_cast_fp16")]; + tensor input_631_axes_0 = const()[name = string("input_631_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127098368)))]; + tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127100480)))]; + tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127102592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128675520))))[name = string("module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_631_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128741120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130314048))))[name = string("module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_635_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2160_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2159_to_fp16)[name = string("op_2160_cast_fp16")]; + tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2160_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor input_643_axes_0 = const()[name = string("input_643_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = string("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130330496)))]; + tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = string("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130332608)))]; + tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = string("input_643_cast_fp16")]; + tensor input_645_axes_0 = const()[name = string("input_645_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130334720)))]; + tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130336832)))]; + tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = string("input_645_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130338944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131911872))))[name = string("module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_645_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131977472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133550400))))[name = string("module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_649_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_2188_to_fp16 = const()[name = string("op_2188_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2189_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2188_to_fp16)[name = string("op_2189_cast_fp16")]; + tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2189_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133566848)))]; + tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133568960)))]; + tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = string("query_25_cast_fp16")]; + tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133571072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133964352))))[name = string("module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_2205 = const()[name = string("op_2205"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_2205, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133980800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134374080))))[name = string("module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_2209 = const()[name = string("op_2209"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_2209, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134390528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134783808))))[name = string("module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_2213 = const()[name = string("op_2213"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_2213, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134800256)))]; + tensor var_2225_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2225_cast_fp16")]; + tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134802368)))]; + tensor var_2227_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2227_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_271_transpose_x_0 = const()[name = string("x_271_transpose_x_0"), val = bool(false)]; + bool x_271_transpose_y_0 = const()[name = string("x_271_transpose_y_0"), val = bool(false)]; + tensor var_2229_to_fp16 = const()[name = string("op_2229_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134804480)))]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2227_cast_fp16)[name = string("transpose_228")]; + tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2229_to_fp16)[name = string("x_271_cast_fp16")]; + tensor x_273_pad_0 = const()[name = string("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_273_mode_0 = const()[name = string("x_273_mode_0"), val = string("constant")]; + fp16 const_134_to_fp16 = const()[name = string("const_134_to_fp16"), val = fp16(0x0p+0)]; + tensor x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2237 = const()[name = string("op_2237"), val = tensor([1, 8, -1, 188])]; + tensor x_275_cast_fp16 = reshape(shape = var_2237, x = x_273_cast_fp16)[name = string("x_275_cast_fp16")]; + tensor var_2241_begin_0 = const()[name = string("op_2241_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2241_end_0 = const()[name = string("op_2241_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2241_end_mask_0 = const()[name = string("op_2241_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2241_cast_fp16 = slice_by_index(begin = var_2241_begin_0, end = var_2241_end_0, end_mask = var_2241_end_mask_0, x = x_275_cast_fp16)[name = string("op_2241_cast_fp16")]; + tensor var_2242 = const()[name = string("op_2242"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2242, x = var_2241_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2225_cast_fp16)[name = string("transpose_227")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_2251_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2251_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_2251_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = string("scores_51_cast_fp16")]; + tensor var_2257_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = string("op_2257_cast_fp16")]; + tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2257_cast_fp16, cond = mask_3)[name = string("input_657_cast_fp16")]; + bool x_277_transpose_x_0 = const()[name = string("x_277_transpose_x_0"), val = bool(false)]; + bool x_277_transpose_y_0 = const()[name = string("x_277_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")]; + tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor var_2261_perm_0 = const()[name = string("op_2261_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2262 = const()[name = string("op_2262"), val = tensor([1, -1, 1024])]; + tensor var_2261_cast_fp16 = transpose(perm = var_2261_perm_0, x = x_277_cast_fp16)[name = string("transpose_224")]; + tensor input_659_cast_fp16 = reshape(shape = var_2262, x = var_2261_cast_fp16)[name = string("input_659_cast_fp16")]; + tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135572544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135965824))))[name = string("module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_659_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = string("input_663_cast_fp16")]; + tensor x_281_axes_0 = const()[name = string("x_281_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135982272)))]; + tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135984384)))]; + tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_665_perm_0 = const()[name = string("input_665_perm_0"), val = tensor([0, 2, 1])]; + string input_667_pad_type_0 = const()[name = string("input_667_pad_type_0"), val = string("valid")]; + tensor input_667_strides_0 = const()[name = string("input_667_strides_0"), val = tensor([1])]; + tensor input_667_pad_0 = const()[name = string("input_667_pad_0"), val = tensor([0, 0])]; + tensor input_667_dilations_0 = const()[name = string("input_667_dilations_0"), val = tensor([1])]; + int32 input_667_groups_0 = const()[name = string("input_667_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135986496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136772992))))[name = string("module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = string("transpose_223")]; + tensor input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_665_cast_fp16)[name = string("input_667_cast_fp16")]; + int32 x_283_split_num_splits_0 = const()[name = string("x_283_split_num_splits_0"), val = int32(2)]; + int32 x_283_split_axis_0 = const()[name = string("x_283_split_axis_0"), val = int32(1)]; + tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = string("x_283_split_cast_fp16")]; + tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = string("x_283_split_1_sigmoid_cast_fp16")]; + tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_328)[name = string("input_669_cast_fp16")]; + tensor input_671_pad_0 = const()[name = string("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_671_mode_0 = const()[name = string("input_671_mode_0"), val = string("constant")]; + fp16 const_137_to_fp16 = const()[name = string("const_137_to_fp16"), val = fp16(0x0p+0)]; + tensor input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = string("input_671_cast_fp16")]; + string input_673_pad_type_0 = const()[name = string("input_673_pad_type_0"), val = string("valid")]; + int32 input_673_groups_0 = const()[name = string("input_673_groups_0"), val = int32(1024)]; + tensor input_673_strides_0 = const()[name = string("input_673_strides_0"), val = tensor([1])]; + tensor input_673_pad_0 = const()[name = string("input_673_pad_0"), val = tensor([0, 0])]; + tensor input_673_dilations_0 = const()[name = string("input_673_dilations_0"), val = tensor([1])]; + tensor const_272_to_fp16 = const()[name = string("const_272_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136805824)))]; + tensor const_273_to_fp16 = const()[name = string("const_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136824320)))]; + tensor input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16, x = input_671_cast_fp16)[name = string("input_675_cast_fp16")]; + tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = string("input_677_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136826432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137219712))))[name = string("module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_677_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = string("transpose_222")]; + tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = string("input_681_cast_fp16")]; + tensor input_683_axes_0 = const()[name = string("input_683_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137236160)))]; + tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137238272)))]; + tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137240384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138813312))))[name = string("module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_683_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138878912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140451840))))[name = string("module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_687_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_2322_to_fp16 = const()[name = string("op_2322_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2323_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2322_to_fp16)[name = string("op_2323_cast_fp16")]; + tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2323_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor input_695_axes_0 = const()[name = string("input_695_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = string("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140468288)))]; + tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = string("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140470400)))]; + tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = string("input_695_cast_fp16")]; + tensor input_697_axes_0 = const()[name = string("input_697_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140472512)))]; + tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140474624)))]; + tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = string("input_697_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140476736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142049664))))[name = string("module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_697_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142115264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143688192))))[name = string("module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_701_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_2351_to_fp16 = const()[name = string("op_2351_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2352_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2351_to_fp16)[name = string("op_2352_cast_fp16")]; + tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2352_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143704640)))]; + tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143706752)))]; + tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = string("query_27_cast_fp16")]; + tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143708864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144102144))))[name = string("module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_2368 = const()[name = string("op_2368"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_2368, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144118592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144511872))))[name = string("module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_2372 = const()[name = string("op_2372"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_2372, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144528320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144921600))))[name = string("module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_2376 = const()[name = string("op_2376"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_2376, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144938048)))]; + tensor var_2388_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2388_cast_fp16")]; + tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144940160)))]; + tensor var_2390_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2390_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor var_2392_to_fp16 = const()[name = string("op_2392_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144942272)))]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2390_cast_fp16)[name = string("transpose_221")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2392_to_fp16)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2400 = const()[name = string("op_2400"), val = tensor([1, 8, -1, 188])]; + tensor x_297_cast_fp16 = reshape(shape = var_2400, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2404_begin_0 = const()[name = string("op_2404_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2404_end_0 = const()[name = string("op_2404_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2404_end_mask_0 = const()[name = string("op_2404_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2404_cast_fp16 = slice_by_index(begin = var_2404_begin_0, end = var_2404_end_0, end_mask = var_2404_end_mask_0, x = x_297_cast_fp16)[name = string("op_2404_cast_fp16")]; + tensor var_2405 = const()[name = string("op_2405"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2405, x = var_2404_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2388_cast_fp16)[name = string("transpose_220")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_2414_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2414_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_2414_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = string("scores_55_cast_fp16")]; + tensor var_2420_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = string("op_2420_cast_fp16")]; + tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2420_cast_fp16, cond = mask_3)[name = string("input_709_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2424_perm_0 = const()[name = string("op_2424_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2425 = const()[name = string("op_2425"), val = tensor([1, -1, 1024])]; + tensor var_2424_cast_fp16 = transpose(perm = var_2424_perm_0, x = x_299_cast_fp16)[name = string("transpose_217")]; + tensor input_711_cast_fp16 = reshape(shape = var_2425, x = var_2424_cast_fp16)[name = string("input_711_cast_fp16")]; + tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145710336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146103616))))[name = string("module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_711_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = string("input_715_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146120064)))]; + tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146122176)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_717_perm_0 = const()[name = string("input_717_perm_0"), val = tensor([0, 2, 1])]; + string input_719_pad_type_0 = const()[name = string("input_719_pad_type_0"), val = string("valid")]; + tensor input_719_strides_0 = const()[name = string("input_719_strides_0"), val = tensor([1])]; + tensor input_719_pad_0 = const()[name = string("input_719_pad_0"), val = tensor([0, 0])]; + tensor input_719_dilations_0 = const()[name = string("input_719_dilations_0"), val = tensor([1])]; + int32 input_719_groups_0 = const()[name = string("input_719_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146124288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146910784))))[name = string("module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = string("transpose_216")]; + tensor input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_717_cast_fp16)[name = string("input_719_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_328)[name = string("input_721_cast_fp16")]; + tensor input_723_pad_0 = const()[name = string("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_723_mode_0 = const()[name = string("input_723_mode_0"), val = string("constant")]; + fp16 const_147_to_fp16 = const()[name = string("const_147_to_fp16"), val = fp16(0x0p+0)]; + tensor input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = string("input_723_cast_fp16")]; + string input_725_pad_type_0 = const()[name = string("input_725_pad_type_0"), val = string("valid")]; + int32 input_725_groups_0 = const()[name = string("input_725_groups_0"), val = int32(1024)]; + tensor input_725_strides_0 = const()[name = string("input_725_strides_0"), val = tensor([1])]; + tensor input_725_pad_0 = const()[name = string("input_725_pad_0"), val = tensor([0, 0])]; + tensor input_725_dilations_0 = const()[name = string("input_725_dilations_0"), val = tensor([1])]; + tensor const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146943616)))]; + tensor const_275_to_fp16 = const()[name = string("const_275_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146962112)))]; + tensor input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16, x = input_723_cast_fp16)[name = string("input_727_cast_fp16")]; + tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = string("input_729_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146964224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147357504))))[name = string("module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_729_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = string("transpose_215")]; + tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = string("input_733_cast_fp16")]; + tensor input_735_axes_0 = const()[name = string("input_735_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147373952)))]; + tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147376064)))]; + tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147378176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148951104))))[name = string("module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_735_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149016704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150589632))))[name = string("module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_739_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_2485_to_fp16 = const()[name = string("op_2485_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2486_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2485_to_fp16)[name = string("op_2486_cast_fp16")]; + tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2486_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor input_747_axes_0 = const()[name = string("input_747_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = string("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150606080)))]; + tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = string("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150608192)))]; + tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = string("input_747_cast_fp16")]; + tensor input_749_axes_0 = const()[name = string("input_749_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150610304)))]; + tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150612416)))]; + tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = string("input_749_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150614528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152187456))))[name = string("module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_749_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152253056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153825984))))[name = string("module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_753_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_2514_to_fp16 = const()[name = string("op_2514_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2515_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2514_to_fp16)[name = string("op_2515_cast_fp16")]; + tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2515_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153842432)))]; + tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153844544)))]; + tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = string("query_29_cast_fp16")]; + tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153846656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154239936))))[name = string("module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_2531 = const()[name = string("op_2531"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_2531, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154256384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154649664))))[name = string("module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_2535 = const()[name = string("op_2535"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_2535, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154666112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155059392))))[name = string("module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_2539 = const()[name = string("op_2539"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_2539, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155075840)))]; + tensor var_2551_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2551_cast_fp16")]; + tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155077952)))]; + tensor var_2553_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2553_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_315_transpose_x_0 = const()[name = string("x_315_transpose_x_0"), val = bool(false)]; + bool x_315_transpose_y_0 = const()[name = string("x_315_transpose_y_0"), val = bool(false)]; + tensor var_2555_to_fp16 = const()[name = string("op_2555_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155080064)))]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2553_cast_fp16)[name = string("transpose_214")]; + tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2555_to_fp16)[name = string("x_315_cast_fp16")]; + tensor x_317_pad_0 = const()[name = string("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_317_mode_0 = const()[name = string("x_317_mode_0"), val = string("constant")]; + fp16 const_154_to_fp16 = const()[name = string("const_154_to_fp16"), val = fp16(0x0p+0)]; + tensor x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")]; + tensor var_2563 = const()[name = string("op_2563"), val = tensor([1, 8, -1, 188])]; + tensor x_319_cast_fp16 = reshape(shape = var_2563, x = x_317_cast_fp16)[name = string("x_319_cast_fp16")]; + tensor var_2567_begin_0 = const()[name = string("op_2567_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2567_end_0 = const()[name = string("op_2567_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2567_end_mask_0 = const()[name = string("op_2567_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2567_cast_fp16 = slice_by_index(begin = var_2567_begin_0, end = var_2567_end_0, end_mask = var_2567_end_mask_0, x = x_319_cast_fp16)[name = string("op_2567_cast_fp16")]; + tensor var_2568 = const()[name = string("op_2568"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2568, x = var_2567_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2551_cast_fp16)[name = string("transpose_213")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_2577_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_2577_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_2577_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = string("scores_59_cast_fp16")]; + tensor var_2583_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = string("op_2583_cast_fp16")]; + tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2583_cast_fp16, cond = mask_3)[name = string("input_761_cast_fp16")]; + bool x_321_transpose_x_0 = const()[name = string("x_321_transpose_x_0"), val = bool(false)]; + bool x_321_transpose_y_0 = const()[name = string("x_321_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")]; + tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_2587_perm_0 = const()[name = string("op_2587_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2588 = const()[name = string("op_2588"), val = tensor([1, -1, 1024])]; + tensor var_2587_cast_fp16 = transpose(perm = var_2587_perm_0, x = x_321_cast_fp16)[name = string("transpose_210")]; + tensor input_763_cast_fp16 = reshape(shape = var_2588, x = var_2587_cast_fp16)[name = string("input_763_cast_fp16")]; + tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155848128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156241408))))[name = string("module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_763_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = string("input_767_cast_fp16")]; + tensor x_325_axes_0 = const()[name = string("x_325_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156257856)))]; + tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156259968)))]; + tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor input_769_perm_0 = const()[name = string("input_769_perm_0"), val = tensor([0, 2, 1])]; + string input_771_pad_type_0 = const()[name = string("input_771_pad_type_0"), val = string("valid")]; + tensor input_771_strides_0 = const()[name = string("input_771_strides_0"), val = tensor([1])]; + tensor input_771_pad_0 = const()[name = string("input_771_pad_0"), val = tensor([0, 0])]; + tensor input_771_dilations_0 = const()[name = string("input_771_dilations_0"), val = tensor([1])]; + int32 input_771_groups_0 = const()[name = string("input_771_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156262080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157048576))))[name = string("module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = string("transpose_209")]; + tensor input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_769_cast_fp16)[name = string("input_771_cast_fp16")]; + int32 x_327_split_num_splits_0 = const()[name = string("x_327_split_num_splits_0"), val = int32(2)]; + int32 x_327_split_axis_0 = const()[name = string("x_327_split_axis_0"), val = int32(1)]; + tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = string("x_327_split_cast_fp16")]; + tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = string("x_327_split_1_sigmoid_cast_fp16")]; + tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = string("x_327_cast_fp16")]; + tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_328)[name = string("input_773_cast_fp16")]; + tensor input_775_pad_0 = const()[name = string("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_775_mode_0 = const()[name = string("input_775_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = string("input_775_cast_fp16")]; + string input_777_pad_type_0 = const()[name = string("input_777_pad_type_0"), val = string("valid")]; + int32 input_777_groups_0 = const()[name = string("input_777_groups_0"), val = int32(1024)]; + tensor input_777_strides_0 = const()[name = string("input_777_strides_0"), val = tensor([1])]; + tensor input_777_pad_0 = const()[name = string("input_777_pad_0"), val = tensor([0, 0])]; + tensor input_777_dilations_0 = const()[name = string("input_777_dilations_0"), val = tensor([1])]; + tensor const_276_to_fp16 = const()[name = string("const_276_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157081408)))]; + tensor const_277_to_fp16 = const()[name = string("const_277_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157099904)))]; + tensor input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16, x = input_775_cast_fp16)[name = string("input_779_cast_fp16")]; + tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = string("input_781_cast_fp16")]; + string x_329_pad_type_0 = const()[name = string("x_329_pad_type_0"), val = string("valid")]; + tensor x_329_strides_0 = const()[name = string("x_329_strides_0"), val = tensor([1])]; + tensor x_329_pad_0 = const()[name = string("x_329_pad_0"), val = tensor([0, 0])]; + tensor x_329_dilations_0 = const()[name = string("x_329_dilations_0"), val = tensor([1])]; + int32 x_329_groups_0 = const()[name = string("x_329_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157102016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157495296))))[name = string("module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_781_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = string("transpose_208")]; + tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = string("input_785_cast_fp16")]; + tensor input_787_axes_0 = const()[name = string("input_787_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157511744)))]; + tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157513856)))]; + tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157515968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159088896))))[name = string("module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_787_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159154496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160727424))))[name = string("module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_791_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_2648_to_fp16 = const()[name = string("op_2648_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2649_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2648_to_fp16)[name = string("op_2649_cast_fp16")]; + tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2649_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor input_799_axes_0 = const()[name = string("input_799_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = string("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160743872)))]; + tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = string("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160745984)))]; + tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = string("input_799_cast_fp16")]; + tensor input_801_axes_0 = const()[name = string("input_801_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160748096)))]; + tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160750208)))]; + tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = string("input_801_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160752320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162325248))))[name = string("module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_801_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162390848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163963776))))[name = string("module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_805_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_2677_to_fp16 = const()[name = string("op_2677_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2678_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2677_to_fp16)[name = string("op_2678_cast_fp16")]; + tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2678_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163980224)))]; + tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163982336)))]; + tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = string("query_31_cast_fp16")]; + tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163984448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164377728))))[name = string("module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_2694 = const()[name = string("op_2694"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_2694, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164394176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164787456))))[name = string("module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_2698 = const()[name = string("op_2698"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_2698, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164803904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165197184))))[name = string("module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_2702 = const()[name = string("op_2702"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_2702, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165213632)))]; + tensor var_2714_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_2714_cast_fp16")]; + tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165215744)))]; + tensor var_2716_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_2716_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_337_transpose_x_0 = const()[name = string("x_337_transpose_x_0"), val = bool(false)]; + bool x_337_transpose_y_0 = const()[name = string("x_337_transpose_y_0"), val = bool(false)]; + tensor var_2718_to_fp16 = const()[name = string("op_2718_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165217856)))]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2716_cast_fp16)[name = string("transpose_207")]; + tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_2718_to_fp16)[name = string("x_337_cast_fp16")]; + tensor x_339_pad_0 = const()[name = string("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_339_mode_0 = const()[name = string("x_339_mode_0"), val = string("constant")]; + fp16 const_164_to_fp16 = const()[name = string("const_164_to_fp16"), val = fp16(0x0p+0)]; + tensor x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")]; + tensor var_2726 = const()[name = string("op_2726"), val = tensor([1, 8, -1, 188])]; + tensor x_341_cast_fp16 = reshape(shape = var_2726, x = x_339_cast_fp16)[name = string("x_341_cast_fp16")]; + tensor var_2730_begin_0 = const()[name = string("op_2730_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2730_end_0 = const()[name = string("op_2730_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2730_end_mask_0 = const()[name = string("op_2730_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2730_cast_fp16 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_341_cast_fp16)[name = string("op_2730_cast_fp16")]; + tensor var_2731 = const()[name = string("op_2731"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_2731, x = var_2730_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2714_cast_fp16)[name = string("transpose_206")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_2740_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_2740_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_2740_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = string("scores_63_cast_fp16")]; + tensor var_2746_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = string("op_2746_cast_fp16")]; + tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2746_cast_fp16, cond = mask_3)[name = string("input_813_cast_fp16")]; + bool x_343_transpose_x_0 = const()[name = string("x_343_transpose_x_0"), val = bool(false)]; + bool x_343_transpose_y_0 = const()[name = string("x_343_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")]; + tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = string("x_343_cast_fp16")]; + tensor var_2750_perm_0 = const()[name = string("op_2750_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2751 = const()[name = string("op_2751"), val = tensor([1, -1, 1024])]; + tensor var_2750_cast_fp16 = transpose(perm = var_2750_perm_0, x = x_343_cast_fp16)[name = string("transpose_203")]; + tensor input_815_cast_fp16 = reshape(shape = var_2751, x = var_2750_cast_fp16)[name = string("input_815_cast_fp16")]; + tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165985920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166379200))))[name = string("module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_815_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = string("input_819_cast_fp16")]; + tensor x_347_axes_0 = const()[name = string("x_347_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166395648)))]; + tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166397760)))]; + tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor input_821_perm_0 = const()[name = string("input_821_perm_0"), val = tensor([0, 2, 1])]; + string input_823_pad_type_0 = const()[name = string("input_823_pad_type_0"), val = string("valid")]; + tensor input_823_strides_0 = const()[name = string("input_823_strides_0"), val = tensor([1])]; + tensor input_823_pad_0 = const()[name = string("input_823_pad_0"), val = tensor([0, 0])]; + tensor input_823_dilations_0 = const()[name = string("input_823_dilations_0"), val = tensor([1])]; + int32 input_823_groups_0 = const()[name = string("input_823_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166399872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167186368))))[name = string("module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = string("transpose_202")]; + tensor input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_821_cast_fp16)[name = string("input_823_cast_fp16")]; + int32 x_349_split_num_splits_0 = const()[name = string("x_349_split_num_splits_0"), val = int32(2)]; + int32 x_349_split_axis_0 = const()[name = string("x_349_split_axis_0"), val = int32(1)]; + tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = string("x_349_split_cast_fp16")]; + tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = string("x_349_split_1_sigmoid_cast_fp16")]; + tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_328)[name = string("input_825_cast_fp16")]; + tensor input_827_pad_0 = const()[name = string("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_827_mode_0 = const()[name = string("input_827_mode_0"), val = string("constant")]; + fp16 const_167_to_fp16 = const()[name = string("const_167_to_fp16"), val = fp16(0x0p+0)]; + tensor input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = string("input_827_cast_fp16")]; + string input_829_pad_type_0 = const()[name = string("input_829_pad_type_0"), val = string("valid")]; + int32 input_829_groups_0 = const()[name = string("input_829_groups_0"), val = int32(1024)]; + tensor input_829_strides_0 = const()[name = string("input_829_strides_0"), val = tensor([1])]; + tensor input_829_pad_0 = const()[name = string("input_829_pad_0"), val = tensor([0, 0])]; + tensor input_829_dilations_0 = const()[name = string("input_829_dilations_0"), val = tensor([1])]; + tensor const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167219200)))]; + tensor const_279_to_fp16 = const()[name = string("const_279_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167237696)))]; + tensor input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16, x = input_827_cast_fp16)[name = string("input_831_cast_fp16")]; + tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = string("input_833_cast_fp16")]; + string x_351_pad_type_0 = const()[name = string("x_351_pad_type_0"), val = string("valid")]; + tensor x_351_strides_0 = const()[name = string("x_351_strides_0"), val = tensor([1])]; + tensor x_351_pad_0 = const()[name = string("x_351_pad_0"), val = tensor([0, 0])]; + tensor x_351_dilations_0 = const()[name = string("x_351_dilations_0"), val = tensor([1])]; + int32 x_351_groups_0 = const()[name = string("x_351_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167239808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167633088))))[name = string("module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_833_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = string("transpose_201")]; + tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = string("input_837_cast_fp16")]; + tensor input_839_axes_0 = const()[name = string("input_839_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167649536)))]; + tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167651648)))]; + tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167653760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169226688))))[name = string("module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_839_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169292288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170865216))))[name = string("module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_843_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_2811_to_fp16 = const()[name = string("op_2811_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2812_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2811_to_fp16)[name = string("op_2812_cast_fp16")]; + tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2812_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor input_851_axes_0 = const()[name = string("input_851_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = string("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170881664)))]; + tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = string("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170883776)))]; + tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = string("input_851_cast_fp16")]; + tensor input_853_axes_0 = const()[name = string("input_853_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170885888)))]; + tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170888000)))]; + tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = string("input_853_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170890112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172463040))))[name = string("module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_853_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172528640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174101568))))[name = string("module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_857_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_2840_to_fp16 = const()[name = string("op_2840_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2841_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2840_to_fp16)[name = string("op_2841_cast_fp16")]; + tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2841_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174118016)))]; + tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174120128)))]; + tensor query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = string("query_33_cast_fp16")]; + tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174122240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174515520))))[name = string("module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_2857 = const()[name = string("op_2857"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_2857, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174531968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174925248))))[name = string("module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_2861 = const()[name = string("op_2861"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_2861, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174941696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175334976))))[name = string("module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_2865 = const()[name = string("op_2865"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_2865, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175351424)))]; + tensor var_2877_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_2877_cast_fp16")]; + tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175353536)))]; + tensor var_2879_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_2879_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_359_transpose_x_0 = const()[name = string("x_359_transpose_x_0"), val = bool(false)]; + bool x_359_transpose_y_0 = const()[name = string("x_359_transpose_y_0"), val = bool(false)]; + tensor var_2881_to_fp16 = const()[name = string("op_2881_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175355648)))]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2879_cast_fp16)[name = string("transpose_200")]; + tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = var_2881_to_fp16)[name = string("x_359_cast_fp16")]; + tensor x_361_pad_0 = const()[name = string("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_361_mode_0 = const()[name = string("x_361_mode_0"), val = string("constant")]; + fp16 const_174_to_fp16 = const()[name = string("const_174_to_fp16"), val = fp16(0x0p+0)]; + tensor x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor var_2889 = const()[name = string("op_2889"), val = tensor([1, 8, -1, 188])]; + tensor x_363_cast_fp16 = reshape(shape = var_2889, x = x_361_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor var_2893_begin_0 = const()[name = string("op_2893_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2893_end_0 = const()[name = string("op_2893_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2893_end_mask_0 = const()[name = string("op_2893_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2893_cast_fp16 = slice_by_index(begin = var_2893_begin_0, end = var_2893_end_0, end_mask = var_2893_end_mask_0, x = x_363_cast_fp16)[name = string("op_2893_cast_fp16")]; + tensor var_2894 = const()[name = string("op_2894"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_2894, x = var_2893_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2877_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_2903_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_2903_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_2903_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = string("scores_67_cast_fp16")]; + tensor var_2909_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = string("op_2909_cast_fp16")]; + tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_2909_cast_fp16, cond = mask_3)[name = string("input_865_cast_fp16")]; + bool x_365_transpose_x_0 = const()[name = string("x_365_transpose_x_0"), val = bool(false)]; + bool x_365_transpose_y_0 = const()[name = string("x_365_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")]; + tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_35_cast_fp16)[name = string("x_365_cast_fp16")]; + tensor var_2913_perm_0 = const()[name = string("op_2913_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2914 = const()[name = string("op_2914"), val = tensor([1, -1, 1024])]; + tensor var_2913_cast_fp16 = transpose(perm = var_2913_perm_0, x = x_365_cast_fp16)[name = string("transpose_196")]; + tensor input_867_cast_fp16 = reshape(shape = var_2914, x = var_2913_cast_fp16)[name = string("input_867_cast_fp16")]; + tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176123712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176516992))))[name = string("module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_867_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = string("input_871_cast_fp16")]; + tensor x_369_axes_0 = const()[name = string("x_369_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176533440)))]; + tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176535552)))]; + tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = string("x_369_cast_fp16")]; + tensor input_873_perm_0 = const()[name = string("input_873_perm_0"), val = tensor([0, 2, 1])]; + string input_875_pad_type_0 = const()[name = string("input_875_pad_type_0"), val = string("valid")]; + tensor input_875_strides_0 = const()[name = string("input_875_strides_0"), val = tensor([1])]; + tensor input_875_pad_0 = const()[name = string("input_875_pad_0"), val = tensor([0, 0])]; + tensor input_875_dilations_0 = const()[name = string("input_875_dilations_0"), val = tensor([1])]; + int32 input_875_groups_0 = const()[name = string("input_875_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176537664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177324160))))[name = string("module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = string("transpose_195")]; + tensor input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_873_cast_fp16)[name = string("input_875_cast_fp16")]; + int32 x_371_split_num_splits_0 = const()[name = string("x_371_split_num_splits_0"), val = int32(2)]; + int32 x_371_split_axis_0 = const()[name = string("x_371_split_axis_0"), val = int32(1)]; + tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = string("x_371_split_cast_fp16")]; + tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = string("x_371_split_1_sigmoid_cast_fp16")]; + tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = string("x_371_cast_fp16")]; + tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_328)[name = string("input_877_cast_fp16")]; + tensor input_879_pad_0 = const()[name = string("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_879_mode_0 = const()[name = string("input_879_mode_0"), val = string("constant")]; + fp16 const_177_to_fp16 = const()[name = string("const_177_to_fp16"), val = fp16(0x0p+0)]; + tensor input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = string("input_879_cast_fp16")]; + string input_881_pad_type_0 = const()[name = string("input_881_pad_type_0"), val = string("valid")]; + int32 input_881_groups_0 = const()[name = string("input_881_groups_0"), val = int32(1024)]; + tensor input_881_strides_0 = const()[name = string("input_881_strides_0"), val = tensor([1])]; + tensor input_881_pad_0 = const()[name = string("input_881_pad_0"), val = tensor([0, 0])]; + tensor input_881_dilations_0 = const()[name = string("input_881_dilations_0"), val = tensor([1])]; + tensor const_280_to_fp16 = const()[name = string("const_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177356992)))]; + tensor const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177375488)))]; + tensor input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16, x = input_879_cast_fp16)[name = string("input_883_cast_fp16")]; + tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = string("input_885_cast_fp16")]; + string x_373_pad_type_0 = const()[name = string("x_373_pad_type_0"), val = string("valid")]; + tensor x_373_strides_0 = const()[name = string("x_373_strides_0"), val = tensor([1])]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0])]; + tensor x_373_dilations_0 = const()[name = string("x_373_dilations_0"), val = tensor([1])]; + int32 x_373_groups_0 = const()[name = string("x_373_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177377600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177770880))))[name = string("module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_885_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = string("transpose_194")]; + tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = string("input_889_cast_fp16")]; + tensor input_891_axes_0 = const()[name = string("input_891_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177787328)))]; + tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177789440)))]; + tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177791552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179364480))))[name = string("module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_891_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179430080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181003008))))[name = string("module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_895_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_2974_to_fp16 = const()[name = string("op_2974_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2975_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2974_to_fp16)[name = string("op_2975_cast_fp16")]; + tensor input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2975_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor input_903_axes_0 = const()[name = string("input_903_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = string("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181019456)))]; + tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = string("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181021568)))]; + tensor input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = string("input_903_cast_fp16")]; + tensor input_905_axes_0 = const()[name = string("input_905_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181023680)))]; + tensor module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181025792)))]; + tensor input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = string("input_905_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181027904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182600832))))[name = string("module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_905_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182666432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184239360))))[name = string("module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_909_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_3003_to_fp16 = const()[name = string("op_3003_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3004_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3003_to_fp16)[name = string("op_3004_cast_fp16")]; + tensor input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3004_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184255808)))]; + tensor module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184257920)))]; + tensor query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = string("query_35_cast_fp16")]; + tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184260032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184653312))))[name = string("module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_3020 = const()[name = string("op_3020"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_3020, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184669760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185063040))))[name = string("module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_3024 = const()[name = string("op_3024"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_3024, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185079488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185472768))))[name = string("module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_3028 = const()[name = string("op_3028"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_3028, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185489216)))]; + tensor var_3040_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3040_cast_fp16")]; + tensor module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185491328)))]; + tensor var_3042_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3042_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_381_transpose_x_0 = const()[name = string("x_381_transpose_x_0"), val = bool(false)]; + bool x_381_transpose_y_0 = const()[name = string("x_381_transpose_y_0"), val = bool(false)]; + tensor var_3044_to_fp16 = const()[name = string("op_3044_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185493440)))]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3042_cast_fp16)[name = string("transpose_193")]; + tensor x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = var_3044_to_fp16)[name = string("x_381_cast_fp16")]; + tensor x_383_pad_0 = const()[name = string("x_383_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_383_mode_0 = const()[name = string("x_383_mode_0"), val = string("constant")]; + fp16 const_184_to_fp16 = const()[name = string("const_184_to_fp16"), val = fp16(0x0p+0)]; + tensor x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor var_3052 = const()[name = string("op_3052"), val = tensor([1, 8, -1, 188])]; + tensor x_385_cast_fp16 = reshape(shape = var_3052, x = x_383_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor var_3056_begin_0 = const()[name = string("op_3056_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3056_end_0 = const()[name = string("op_3056_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3056_end_mask_0 = const()[name = string("op_3056_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3056_cast_fp16 = slice_by_index(begin = var_3056_begin_0, end = var_3056_end_0, end_mask = var_3056_end_mask_0, x = x_385_cast_fp16)[name = string("op_3056_cast_fp16")]; + tensor var_3057 = const()[name = string("op_3057"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3057, x = var_3056_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3040_cast_fp16)[name = string("transpose_192")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_3066_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3066_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_3066_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = string("scores_71_cast_fp16")]; + tensor var_3072_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = string("op_3072_cast_fp16")]; + tensor input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3072_cast_fp16, cond = mask_3)[name = string("input_917_cast_fp16")]; + bool x_387_transpose_x_0 = const()[name = string("x_387_transpose_x_0"), val = bool(false)]; + bool x_387_transpose_y_0 = const()[name = string("x_387_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")]; + tensor x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_37_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor var_3076_perm_0 = const()[name = string("op_3076_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3077 = const()[name = string("op_3077"), val = tensor([1, -1, 1024])]; + tensor var_3076_cast_fp16 = transpose(perm = var_3076_perm_0, x = x_387_cast_fp16)[name = string("transpose_189")]; + tensor input_919_cast_fp16 = reshape(shape = var_3077, x = var_3076_cast_fp16)[name = string("input_919_cast_fp16")]; + tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186261504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186654784))))[name = string("module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_919_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = string("input_923_cast_fp16")]; + tensor x_391_axes_0 = const()[name = string("x_391_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("module_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186671232)))]; + tensor module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("module_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186673344)))]; + tensor x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = string("x_391_cast_fp16")]; + tensor input_925_perm_0 = const()[name = string("input_925_perm_0"), val = tensor([0, 2, 1])]; + string input_927_pad_type_0 = const()[name = string("input_927_pad_type_0"), val = string("valid")]; + tensor input_927_strides_0 = const()[name = string("input_927_strides_0"), val = tensor([1])]; + tensor input_927_pad_0 = const()[name = string("input_927_pad_0"), val = tensor([0, 0])]; + tensor input_927_dilations_0 = const()[name = string("input_927_dilations_0"), val = tensor([1])]; + int32 input_927_groups_0 = const()[name = string("input_927_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186675456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187461952))))[name = string("module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = string("transpose_188")]; + tensor input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_925_cast_fp16)[name = string("input_927_cast_fp16")]; + int32 x_393_split_num_splits_0 = const()[name = string("x_393_split_num_splits_0"), val = int32(2)]; + int32 x_393_split_axis_0 = const()[name = string("x_393_split_axis_0"), val = int32(1)]; + tensor x_393_split_cast_fp16_0, tensor x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = string("x_393_split_cast_fp16")]; + tensor x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = string("x_393_split_1_sigmoid_cast_fp16")]; + tensor x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = string("x_393_cast_fp16")]; + tensor input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_328)[name = string("input_929_cast_fp16")]; + tensor input_931_pad_0 = const()[name = string("input_931_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_931_mode_0 = const()[name = string("input_931_mode_0"), val = string("constant")]; + fp16 const_187_to_fp16 = const()[name = string("const_187_to_fp16"), val = fp16(0x0p+0)]; + tensor input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = string("input_931_cast_fp16")]; + string input_933_pad_type_0 = const()[name = string("input_933_pad_type_0"), val = string("valid")]; + int32 input_933_groups_0 = const()[name = string("input_933_groups_0"), val = int32(1024)]; + tensor input_933_strides_0 = const()[name = string("input_933_strides_0"), val = tensor([1])]; + tensor input_933_pad_0 = const()[name = string("input_933_pad_0"), val = tensor([0, 0])]; + tensor input_933_dilations_0 = const()[name = string("input_933_dilations_0"), val = tensor([1])]; + tensor const_282_to_fp16 = const()[name = string("const_282_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187494784)))]; + tensor const_283_to_fp16 = const()[name = string("const_283_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187513280)))]; + tensor input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16, x = input_931_cast_fp16)[name = string("input_935_cast_fp16")]; + tensor input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = string("input_937_cast_fp16")]; + string x_395_pad_type_0 = const()[name = string("x_395_pad_type_0"), val = string("valid")]; + tensor x_395_strides_0 = const()[name = string("x_395_strides_0"), val = tensor([1])]; + tensor x_395_pad_0 = const()[name = string("x_395_pad_0"), val = tensor([0, 0])]; + tensor x_395_dilations_0 = const()[name = string("x_395_dilations_0"), val = tensor([1])]; + int32 x_395_groups_0 = const()[name = string("x_395_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187515392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187908672))))[name = string("module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_937_cast_fp16)[name = string("x_395_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = string("transpose_187")]; + tensor input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = string("input_941_cast_fp16")]; + tensor input_943_axes_0 = const()[name = string("input_943_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187925120)))]; + tensor module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187927232)))]; + tensor input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187929344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189502272))))[name = string("module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_943_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189567872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191140800))))[name = string("module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_947_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_3137_to_fp16 = const()[name = string("op_3137_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3138_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3137_to_fp16)[name = string("op_3138_cast_fp16")]; + tensor input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3138_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor input_955_axes_0 = const()[name = string("input_955_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_out_weight_to_fp16 = const()[name = string("module_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191157248)))]; + tensor module_layers_17_norm_out_bias_to_fp16 = const()[name = string("module_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191159360)))]; + tensor input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = string("input_955_cast_fp16")]; + tensor input_957_axes_0 = const()[name = string("input_957_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191161472)))]; + tensor module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191163584)))]; + tensor input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = string("input_957_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191165696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192738624))))[name = string("module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_957_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192804224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194377152))))[name = string("module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_961_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3167_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3166_to_fp16)[name = string("op_3167_cast_fp16")]; + tensor input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3167_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194393600)))]; + tensor module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194395712)))]; + tensor query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = string("query_37_cast_fp16")]; + tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194397824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194791104))))[name = string("module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_3183 = const()[name = string("op_3183"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_3183, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194807552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195200832))))[name = string("module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_3187 = const()[name = string("op_3187"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_3187, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195217280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195610560))))[name = string("module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_3191 = const()[name = string("op_3191"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_3191, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195627008)))]; + tensor var_3203_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3203_cast_fp16")]; + tensor module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195629120)))]; + tensor var_3205_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3205_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor var_3207_to_fp16 = const()[name = string("op_3207_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195631232)))]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3205_cast_fp16)[name = string("transpose_186")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = var_3207_to_fp16)[name = string("x_403_cast_fp16")]; + tensor x_405_pad_0 = const()[name = string("x_405_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_405_mode_0 = const()[name = string("x_405_mode_0"), val = string("constant")]; + fp16 const_194_to_fp16 = const()[name = string("const_194_to_fp16"), val = fp16(0x0p+0)]; + tensor x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")]; + tensor var_3215 = const()[name = string("op_3215"), val = tensor([1, 8, -1, 188])]; + tensor x_407_cast_fp16 = reshape(shape = var_3215, x = x_405_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor var_3219_begin_0 = const()[name = string("op_3219_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3219_end_0 = const()[name = string("op_3219_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3219_end_mask_0 = const()[name = string("op_3219_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3219_cast_fp16 = slice_by_index(begin = var_3219_begin_0, end = var_3219_end_0, end_mask = var_3219_end_mask_0, x = x_407_cast_fp16)[name = string("op_3219_cast_fp16")]; + tensor var_3220 = const()[name = string("op_3220"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_3220, x = var_3219_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3203_cast_fp16)[name = string("transpose_185")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_3229_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3229_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_3229_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = string("scores_75_cast_fp16")]; + tensor var_3235_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = string("op_3235_cast_fp16")]; + tensor input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3235_cast_fp16, cond = mask_3)[name = string("input_969_cast_fp16")]; + bool x_409_transpose_x_0 = const()[name = string("x_409_transpose_x_0"), val = bool(false)]; + bool x_409_transpose_y_0 = const()[name = string("x_409_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")]; + tensor x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_39_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor var_3239_perm_0 = const()[name = string("op_3239_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3240 = const()[name = string("op_3240"), val = tensor([1, -1, 1024])]; + tensor var_3239_cast_fp16 = transpose(perm = var_3239_perm_0, x = x_409_cast_fp16)[name = string("transpose_182")]; + tensor input_971_cast_fp16 = reshape(shape = var_3240, x = var_3239_cast_fp16)[name = string("input_971_cast_fp16")]; + tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196399296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196792576))))[name = string("module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_971_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = string("input_975_cast_fp16")]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("module_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196809024)))]; + tensor module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("module_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196811136)))]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_977_perm_0 = const()[name = string("input_977_perm_0"), val = tensor([0, 2, 1])]; + string input_979_pad_type_0 = const()[name = string("input_979_pad_type_0"), val = string("valid")]; + tensor input_979_strides_0 = const()[name = string("input_979_strides_0"), val = tensor([1])]; + tensor input_979_pad_0 = const()[name = string("input_979_pad_0"), val = tensor([0, 0])]; + tensor input_979_dilations_0 = const()[name = string("input_979_dilations_0"), val = tensor([1])]; + int32 input_979_groups_0 = const()[name = string("input_979_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196813248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197599744))))[name = string("module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = string("transpose_181")]; + tensor input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_977_cast_fp16)[name = string("input_979_cast_fp16")]; + int32 x_415_split_num_splits_0 = const()[name = string("x_415_split_num_splits_0"), val = int32(2)]; + int32 x_415_split_axis_0 = const()[name = string("x_415_split_axis_0"), val = int32(1)]; + tensor x_415_split_cast_fp16_0, tensor x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = string("x_415_split_cast_fp16")]; + tensor x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = string("x_415_split_1_sigmoid_cast_fp16")]; + tensor x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_328)[name = string("input_981_cast_fp16")]; + tensor input_983_pad_0 = const()[name = string("input_983_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_983_mode_0 = const()[name = string("input_983_mode_0"), val = string("constant")]; + fp16 const_197_to_fp16 = const()[name = string("const_197_to_fp16"), val = fp16(0x0p+0)]; + tensor input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = string("input_983_cast_fp16")]; + string input_985_pad_type_0 = const()[name = string("input_985_pad_type_0"), val = string("valid")]; + int32 input_985_groups_0 = const()[name = string("input_985_groups_0"), val = int32(1024)]; + tensor input_985_strides_0 = const()[name = string("input_985_strides_0"), val = tensor([1])]; + tensor input_985_pad_0 = const()[name = string("input_985_pad_0"), val = tensor([0, 0])]; + tensor input_985_dilations_0 = const()[name = string("input_985_dilations_0"), val = tensor([1])]; + tensor const_284_to_fp16 = const()[name = string("const_284_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197632576)))]; + tensor const_285_to_fp16 = const()[name = string("const_285_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197651072)))]; + tensor input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16, x = input_983_cast_fp16)[name = string("input_987_cast_fp16")]; + tensor input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = string("input_989_cast_fp16")]; + string x_417_pad_type_0 = const()[name = string("x_417_pad_type_0"), val = string("valid")]; + tensor x_417_strides_0 = const()[name = string("x_417_strides_0"), val = tensor([1])]; + tensor x_417_pad_0 = const()[name = string("x_417_pad_0"), val = tensor([0, 0])]; + tensor x_417_dilations_0 = const()[name = string("x_417_dilations_0"), val = tensor([1])]; + int32 x_417_groups_0 = const()[name = string("x_417_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197653184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198046464))))[name = string("module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_989_cast_fp16)[name = string("x_417_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = string("transpose_180")]; + tensor input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = string("input_993_cast_fp16")]; + tensor input_995_axes_0 = const()[name = string("input_995_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198062912)))]; + tensor module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198065024)))]; + tensor input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198067136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199640064))))[name = string("module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_995_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199705664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201278592))))[name = string("module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_999_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_3300_to_fp16 = const()[name = string("op_3300_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3301_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3300_to_fp16)[name = string("op_3301_cast_fp16")]; + tensor input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3301_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor input_1007_axes_0 = const()[name = string("input_1007_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_out_weight_to_fp16 = const()[name = string("module_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201295040)))]; + tensor module_layers_18_norm_out_bias_to_fp16 = const()[name = string("module_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201297152)))]; + tensor input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = string("input_1007_cast_fp16")]; + tensor input_1009_axes_0 = const()[name = string("input_1009_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201299264)))]; + tensor module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201301376)))]; + tensor input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = string("input_1009_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201303488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202876416))))[name = string("module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1009_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202942016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204514944))))[name = string("module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1013_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_3329_to_fp16 = const()[name = string("op_3329_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3330_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3329_to_fp16)[name = string("op_3330_cast_fp16")]; + tensor input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3330_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204531392)))]; + tensor module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204533504)))]; + tensor query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = string("query_39_cast_fp16")]; + tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204535616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204928896))))[name = string("module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_3346 = const()[name = string("op_3346"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_3346, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204945344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205338624))))[name = string("module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_3350 = const()[name = string("op_3350"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_3350, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205355072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205748352))))[name = string("module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_3354 = const()[name = string("op_3354"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_3354, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205764800)))]; + tensor var_3366_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3366_cast_fp16")]; + tensor module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205766912)))]; + tensor var_3368_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3368_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_425_transpose_x_0 = const()[name = string("x_425_transpose_x_0"), val = bool(false)]; + bool x_425_transpose_y_0 = const()[name = string("x_425_transpose_y_0"), val = bool(false)]; + tensor var_3370_to_fp16 = const()[name = string("op_3370_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205769024)))]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3368_cast_fp16)[name = string("transpose_179")]; + tensor x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = var_3370_to_fp16)[name = string("x_425_cast_fp16")]; + tensor x_427_pad_0 = const()[name = string("x_427_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_427_mode_0 = const()[name = string("x_427_mode_0"), val = string("constant")]; + fp16 const_204_to_fp16 = const()[name = string("const_204_to_fp16"), val = fp16(0x0p+0)]; + tensor x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3378 = const()[name = string("op_3378"), val = tensor([1, 8, -1, 188])]; + tensor x_429_cast_fp16 = reshape(shape = var_3378, x = x_427_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3382_begin_0 = const()[name = string("op_3382_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3382_end_0 = const()[name = string("op_3382_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3382_end_mask_0 = const()[name = string("op_3382_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3382_cast_fp16 = slice_by_index(begin = var_3382_begin_0, end = var_3382_end_0, end_mask = var_3382_end_mask_0, x = x_429_cast_fp16)[name = string("op_3382_cast_fp16")]; + tensor var_3383 = const()[name = string("op_3383"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3383, x = var_3382_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3366_cast_fp16)[name = string("transpose_178")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_3392_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3392_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_3392_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = string("scores_79_cast_fp16")]; + tensor var_3398_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = string("op_3398_cast_fp16")]; + tensor input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3398_cast_fp16, cond = mask_3)[name = string("input_1021_cast_fp16")]; + bool x_431_transpose_x_0 = const()[name = string("x_431_transpose_x_0"), val = bool(false)]; + bool x_431_transpose_y_0 = const()[name = string("x_431_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")]; + tensor x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_41_cast_fp16)[name = string("x_431_cast_fp16")]; + tensor var_3402_perm_0 = const()[name = string("op_3402_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3403 = const()[name = string("op_3403"), val = tensor([1, -1, 1024])]; + tensor var_3402_cast_fp16 = transpose(perm = var_3402_perm_0, x = x_431_cast_fp16)[name = string("transpose_175")]; + tensor input_1023_cast_fp16 = reshape(shape = var_3403, x = var_3402_cast_fp16)[name = string("input_1023_cast_fp16")]; + tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206537088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206930368))))[name = string("module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1023_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1027_cast_fp16")]; + tensor x_435_axes_0 = const()[name = string("x_435_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("module_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206946816)))]; + tensor module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("module_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206948928)))]; + tensor x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_1029_perm_0 = const()[name = string("input_1029_perm_0"), val = tensor([0, 2, 1])]; + string input_1031_pad_type_0 = const()[name = string("input_1031_pad_type_0"), val = string("valid")]; + tensor input_1031_strides_0 = const()[name = string("input_1031_strides_0"), val = tensor([1])]; + tensor input_1031_pad_0 = const()[name = string("input_1031_pad_0"), val = tensor([0, 0])]; + tensor input_1031_dilations_0 = const()[name = string("input_1031_dilations_0"), val = tensor([1])]; + int32 input_1031_groups_0 = const()[name = string("input_1031_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206951040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207737536))))[name = string("module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = string("transpose_174")]; + tensor input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1029_cast_fp16)[name = string("input_1031_cast_fp16")]; + int32 x_437_split_num_splits_0 = const()[name = string("x_437_split_num_splits_0"), val = int32(2)]; + int32 x_437_split_axis_0 = const()[name = string("x_437_split_axis_0"), val = int32(1)]; + tensor x_437_split_cast_fp16_0, tensor x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = string("x_437_split_cast_fp16")]; + tensor x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = string("x_437_split_1_sigmoid_cast_fp16")]; + tensor x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_328)[name = string("input_1033_cast_fp16")]; + tensor input_1035_pad_0 = const()[name = string("input_1035_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1035_mode_0 = const()[name = string("input_1035_mode_0"), val = string("constant")]; + fp16 const_207_to_fp16 = const()[name = string("const_207_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = string("input_1035_cast_fp16")]; + string input_1037_pad_type_0 = const()[name = string("input_1037_pad_type_0"), val = string("valid")]; + int32 input_1037_groups_0 = const()[name = string("input_1037_groups_0"), val = int32(1024)]; + tensor input_1037_strides_0 = const()[name = string("input_1037_strides_0"), val = tensor([1])]; + tensor input_1037_pad_0 = const()[name = string("input_1037_pad_0"), val = tensor([0, 0])]; + tensor input_1037_dilations_0 = const()[name = string("input_1037_dilations_0"), val = tensor([1])]; + tensor const_286_to_fp16 = const()[name = string("const_286_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207770368)))]; + tensor const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207788864)))]; + tensor input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16, x = input_1035_cast_fp16)[name = string("input_1039_cast_fp16")]; + tensor input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = string("input_1041_cast_fp16")]; + string x_439_pad_type_0 = const()[name = string("x_439_pad_type_0"), val = string("valid")]; + tensor x_439_strides_0 = const()[name = string("x_439_strides_0"), val = tensor([1])]; + tensor x_439_pad_0 = const()[name = string("x_439_pad_0"), val = tensor([0, 0])]; + tensor x_439_dilations_0 = const()[name = string("x_439_dilations_0"), val = tensor([1])]; + int32 x_439_groups_0 = const()[name = string("x_439_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207790976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208184256))))[name = string("module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1041_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = string("transpose_173")]; + tensor input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = string("input_1045_cast_fp16")]; + tensor input_1047_axes_0 = const()[name = string("input_1047_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208200704)))]; + tensor module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208202816)))]; + tensor input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208204928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209777856))))[name = string("module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1047_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209843456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211416384))))[name = string("module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1051_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_3463_to_fp16 = const()[name = string("op_3463_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3464_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3463_to_fp16)[name = string("op_3464_cast_fp16")]; + tensor input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3464_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor input_1059_axes_0 = const()[name = string("input_1059_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_out_weight_to_fp16 = const()[name = string("module_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211432832)))]; + tensor module_layers_19_norm_out_bias_to_fp16 = const()[name = string("module_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211434944)))]; + tensor input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = string("input_1059_cast_fp16")]; + tensor input_1061_axes_0 = const()[name = string("input_1061_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211437056)))]; + tensor module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211439168)))]; + tensor input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = string("input_1061_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211441280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213014208))))[name = string("module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1061_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213079808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214652736))))[name = string("module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1065_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_3492_to_fp16 = const()[name = string("op_3492_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3493_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3492_to_fp16)[name = string("op_3493_cast_fp16")]; + tensor input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3493_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214669184)))]; + tensor module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214671296)))]; + tensor query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = string("query_41_cast_fp16")]; + tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214673408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215066688))))[name = string("module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_3509 = const()[name = string("op_3509"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_3509, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215083136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215476416))))[name = string("module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_3513 = const()[name = string("op_3513"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_3513, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215492864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215886144))))[name = string("module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_3517 = const()[name = string("op_3517"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_3517, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215902592)))]; + tensor var_3529_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_3529_cast_fp16")]; + tensor module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215904704)))]; + tensor var_3531_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_3531_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_447_transpose_x_0 = const()[name = string("x_447_transpose_x_0"), val = bool(false)]; + bool x_447_transpose_y_0 = const()[name = string("x_447_transpose_y_0"), val = bool(false)]; + tensor var_3533_to_fp16 = const()[name = string("op_3533_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215906816)))]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3531_cast_fp16)[name = string("transpose_172")]; + tensor x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = var_3533_to_fp16)[name = string("x_447_cast_fp16")]; + tensor x_449_pad_0 = const()[name = string("x_449_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_449_mode_0 = const()[name = string("x_449_mode_0"), val = string("constant")]; + fp16 const_214_to_fp16 = const()[name = string("const_214_to_fp16"), val = fp16(0x0p+0)]; + tensor x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")]; + tensor var_3541 = const()[name = string("op_3541"), val = tensor([1, 8, -1, 188])]; + tensor x_451_cast_fp16 = reshape(shape = var_3541, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_3545_begin_0 = const()[name = string("op_3545_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3545_end_0 = const()[name = string("op_3545_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3545_end_mask_0 = const()[name = string("op_3545_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3545_cast_fp16 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = x_451_cast_fp16)[name = string("op_3545_cast_fp16")]; + tensor var_3546 = const()[name = string("op_3546"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_3546, x = var_3545_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3529_cast_fp16)[name = string("transpose_171")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_3555_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_3555_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_3555_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = string("scores_83_cast_fp16")]; + tensor var_3561_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = string("op_3561_cast_fp16")]; + tensor input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3561_cast_fp16, cond = mask_3)[name = string("input_1073_cast_fp16")]; + bool x_453_transpose_x_0 = const()[name = string("x_453_transpose_x_0"), val = bool(false)]; + bool x_453_transpose_y_0 = const()[name = string("x_453_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")]; + tensor x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_43_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_3565_perm_0 = const()[name = string("op_3565_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3566 = const()[name = string("op_3566"), val = tensor([1, -1, 1024])]; + tensor var_3565_cast_fp16 = transpose(perm = var_3565_perm_0, x = x_453_cast_fp16)[name = string("transpose_168")]; + tensor input_1075_cast_fp16 = reshape(shape = var_3566, x = var_3565_cast_fp16)[name = string("input_1075_cast_fp16")]; + tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216674880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217068160))))[name = string("module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1075_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1079_cast_fp16")]; + tensor x_457_axes_0 = const()[name = string("x_457_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("module_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217084608)))]; + tensor module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("module_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217086720)))]; + tensor x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = string("x_457_cast_fp16")]; + tensor input_1081_perm_0 = const()[name = string("input_1081_perm_0"), val = tensor([0, 2, 1])]; + string input_1083_pad_type_0 = const()[name = string("input_1083_pad_type_0"), val = string("valid")]; + tensor input_1083_strides_0 = const()[name = string("input_1083_strides_0"), val = tensor([1])]; + tensor input_1083_pad_0 = const()[name = string("input_1083_pad_0"), val = tensor([0, 0])]; + tensor input_1083_dilations_0 = const()[name = string("input_1083_dilations_0"), val = tensor([1])]; + int32 input_1083_groups_0 = const()[name = string("input_1083_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217088832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217875328))))[name = string("module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = string("transpose_167")]; + tensor input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1081_cast_fp16)[name = string("input_1083_cast_fp16")]; + int32 x_459_split_num_splits_0 = const()[name = string("x_459_split_num_splits_0"), val = int32(2)]; + int32 x_459_split_axis_0 = const()[name = string("x_459_split_axis_0"), val = int32(1)]; + tensor x_459_split_cast_fp16_0, tensor x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = string("x_459_split_cast_fp16")]; + tensor x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = string("x_459_split_1_sigmoid_cast_fp16")]; + tensor x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_328)[name = string("input_1085_cast_fp16")]; + tensor input_1087_pad_0 = const()[name = string("input_1087_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1087_mode_0 = const()[name = string("input_1087_mode_0"), val = string("constant")]; + fp16 const_217_to_fp16 = const()[name = string("const_217_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = string("input_1087_cast_fp16")]; + string input_1089_pad_type_0 = const()[name = string("input_1089_pad_type_0"), val = string("valid")]; + int32 input_1089_groups_0 = const()[name = string("input_1089_groups_0"), val = int32(1024)]; + tensor input_1089_strides_0 = const()[name = string("input_1089_strides_0"), val = tensor([1])]; + tensor input_1089_pad_0 = const()[name = string("input_1089_pad_0"), val = tensor([0, 0])]; + tensor input_1089_dilations_0 = const()[name = string("input_1089_dilations_0"), val = tensor([1])]; + tensor const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217908160)))]; + tensor const_289_to_fp16 = const()[name = string("const_289_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217926656)))]; + tensor input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16, x = input_1087_cast_fp16)[name = string("input_1091_cast_fp16")]; + tensor input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = string("input_1093_cast_fp16")]; + string x_461_pad_type_0 = const()[name = string("x_461_pad_type_0"), val = string("valid")]; + tensor x_461_strides_0 = const()[name = string("x_461_strides_0"), val = tensor([1])]; + tensor x_461_pad_0 = const()[name = string("x_461_pad_0"), val = tensor([0, 0])]; + tensor x_461_dilations_0 = const()[name = string("x_461_dilations_0"), val = tensor([1])]; + int32 x_461_groups_0 = const()[name = string("x_461_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217928768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218322048))))[name = string("module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1093_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = string("transpose_166")]; + tensor input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = string("input_1097_cast_fp16")]; + tensor input_1099_axes_0 = const()[name = string("input_1099_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218338496)))]; + tensor module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218340608)))]; + tensor input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218342720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219915648))))[name = string("module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1099_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219981248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221554176))))[name = string("module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1103_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_3626_to_fp16 = const()[name = string("op_3626_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3627_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3626_to_fp16)[name = string("op_3627_cast_fp16")]; + tensor input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3627_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor input_1111_axes_0 = const()[name = string("input_1111_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_out_weight_to_fp16 = const()[name = string("module_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221570624)))]; + tensor module_layers_20_norm_out_bias_to_fp16 = const()[name = string("module_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221572736)))]; + tensor input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = string("input_1111_cast_fp16")]; + tensor input_1113_axes_0 = const()[name = string("input_1113_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221574848)))]; + tensor module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221576960)))]; + tensor input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = string("input_1113_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221579072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223152000))))[name = string("module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1113_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223217600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224790528))))[name = string("module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1117_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_3655_to_fp16 = const()[name = string("op_3655_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3656_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3655_to_fp16)[name = string("op_3656_cast_fp16")]; + tensor input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3656_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224806976)))]; + tensor module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224809088)))]; + tensor query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = string("query_43_cast_fp16")]; + tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224811200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225204480))))[name = string("module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_3672 = const()[name = string("op_3672"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_3672, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225220928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225614208))))[name = string("module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_3676 = const()[name = string("op_3676"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_3676, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225630656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226023936))))[name = string("module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_3680 = const()[name = string("op_3680"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_3680, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226040384)))]; + tensor var_3692_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_3692_cast_fp16")]; + tensor module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226042496)))]; + tensor var_3694_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_3694_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_469_transpose_x_0 = const()[name = string("x_469_transpose_x_0"), val = bool(false)]; + bool x_469_transpose_y_0 = const()[name = string("x_469_transpose_y_0"), val = bool(false)]; + tensor var_3696_to_fp16 = const()[name = string("op_3696_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226044608)))]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3694_cast_fp16)[name = string("transpose_165")]; + tensor x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = var_3696_to_fp16)[name = string("x_469_cast_fp16")]; + tensor x_471_pad_0 = const()[name = string("x_471_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_471_mode_0 = const()[name = string("x_471_mode_0"), val = string("constant")]; + fp16 const_224_to_fp16 = const()[name = string("const_224_to_fp16"), val = fp16(0x0p+0)]; + tensor x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")]; + tensor var_3704 = const()[name = string("op_3704"), val = tensor([1, 8, -1, 188])]; + tensor x_473_cast_fp16 = reshape(shape = var_3704, x = x_471_cast_fp16)[name = string("x_473_cast_fp16")]; + tensor var_3708_begin_0 = const()[name = string("op_3708_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3708_end_0 = const()[name = string("op_3708_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3708_end_mask_0 = const()[name = string("op_3708_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3708_cast_fp16 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = x_473_cast_fp16)[name = string("op_3708_cast_fp16")]; + tensor var_3709 = const()[name = string("op_3709"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_3709, x = var_3708_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3692_cast_fp16)[name = string("transpose_164")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_3718_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_3718_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_3718_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = string("scores_87_cast_fp16")]; + tensor var_3724_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = string("op_3724_cast_fp16")]; + tensor input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3724_cast_fp16, cond = mask_3)[name = string("input_1125_cast_fp16")]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_45_cast_fp16)[name = string("x_475_cast_fp16")]; + tensor var_3728_perm_0 = const()[name = string("op_3728_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3729 = const()[name = string("op_3729"), val = tensor([1, -1, 1024])]; + tensor var_3728_cast_fp16 = transpose(perm = var_3728_perm_0, x = x_475_cast_fp16)[name = string("transpose_161")]; + tensor input_1127_cast_fp16 = reshape(shape = var_3729, x = var_3728_cast_fp16)[name = string("input_1127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226812672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227205952))))[name = string("module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1127_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1131_cast_fp16")]; + tensor x_479_axes_0 = const()[name = string("x_479_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("module_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227222400)))]; + tensor module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("module_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227224512)))]; + tensor x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor input_1133_perm_0 = const()[name = string("input_1133_perm_0"), val = tensor([0, 2, 1])]; + string input_1135_pad_type_0 = const()[name = string("input_1135_pad_type_0"), val = string("valid")]; + tensor input_1135_strides_0 = const()[name = string("input_1135_strides_0"), val = tensor([1])]; + tensor input_1135_pad_0 = const()[name = string("input_1135_pad_0"), val = tensor([0, 0])]; + tensor input_1135_dilations_0 = const()[name = string("input_1135_dilations_0"), val = tensor([1])]; + int32 input_1135_groups_0 = const()[name = string("input_1135_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227226624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228013120))))[name = string("module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = string("transpose_160")]; + tensor input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1133_cast_fp16)[name = string("input_1135_cast_fp16")]; + int32 x_481_split_num_splits_0 = const()[name = string("x_481_split_num_splits_0"), val = int32(2)]; + int32 x_481_split_axis_0 = const()[name = string("x_481_split_axis_0"), val = int32(1)]; + tensor x_481_split_cast_fp16_0, tensor x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = string("x_481_split_cast_fp16")]; + tensor x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = string("x_481_split_1_sigmoid_cast_fp16")]; + tensor x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_328)[name = string("input_1137_cast_fp16")]; + tensor input_1139_pad_0 = const()[name = string("input_1139_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1139_mode_0 = const()[name = string("input_1139_mode_0"), val = string("constant")]; + fp16 const_227_to_fp16 = const()[name = string("const_227_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = string("input_1139_cast_fp16")]; + string input_1141_pad_type_0 = const()[name = string("input_1141_pad_type_0"), val = string("valid")]; + int32 input_1141_groups_0 = const()[name = string("input_1141_groups_0"), val = int32(1024)]; + tensor input_1141_strides_0 = const()[name = string("input_1141_strides_0"), val = tensor([1])]; + tensor input_1141_pad_0 = const()[name = string("input_1141_pad_0"), val = tensor([0, 0])]; + tensor input_1141_dilations_0 = const()[name = string("input_1141_dilations_0"), val = tensor([1])]; + tensor const_290_to_fp16 = const()[name = string("const_290_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228045952)))]; + tensor const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228064448)))]; + tensor input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16, x = input_1139_cast_fp16)[name = string("input_1143_cast_fp16")]; + tensor input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = string("input_1145_cast_fp16")]; + string x_483_pad_type_0 = const()[name = string("x_483_pad_type_0"), val = string("valid")]; + tensor x_483_strides_0 = const()[name = string("x_483_strides_0"), val = tensor([1])]; + tensor x_483_pad_0 = const()[name = string("x_483_pad_0"), val = tensor([0, 0])]; + tensor x_483_dilations_0 = const()[name = string("x_483_dilations_0"), val = tensor([1])]; + int32 x_483_groups_0 = const()[name = string("x_483_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228066560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228459840))))[name = string("module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1145_cast_fp16)[name = string("x_483_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = string("transpose_159")]; + tensor input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = string("input_1149_cast_fp16")]; + tensor input_1151_axes_0 = const()[name = string("input_1151_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228476288)))]; + tensor module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228478400)))]; + tensor input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228480512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230053440))))[name = string("module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1151_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230119040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231691968))))[name = string("module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1155_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_3789_to_fp16 = const()[name = string("op_3789_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3790_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3789_to_fp16)[name = string("op_3790_cast_fp16")]; + tensor input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3790_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor input_1163_axes_0 = const()[name = string("input_1163_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_out_weight_to_fp16 = const()[name = string("module_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231708416)))]; + tensor module_layers_21_norm_out_bias_to_fp16 = const()[name = string("module_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231710528)))]; + tensor input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = string("input_1163_cast_fp16")]; + tensor input_1165_axes_0 = const()[name = string("input_1165_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231712640)))]; + tensor module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231714752)))]; + tensor input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = string("input_1165_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231716864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233289792))))[name = string("module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1165_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233355392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234928320))))[name = string("module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1169_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_3818_to_fp16 = const()[name = string("op_3818_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3819_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3818_to_fp16)[name = string("op_3819_cast_fp16")]; + tensor input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3819_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234944768)))]; + tensor module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234946880)))]; + tensor query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = string("query_45_cast_fp16")]; + tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234948992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235342272))))[name = string("module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_3835 = const()[name = string("op_3835"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_3835, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235358720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235752000))))[name = string("module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_3839 = const()[name = string("op_3839"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_3839, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235768448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236161728))))[name = string("module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_3843 = const()[name = string("op_3843"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_3843, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236178176)))]; + tensor var_3855_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_3855_cast_fp16")]; + tensor module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236180288)))]; + tensor var_3857_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_3857_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_491_transpose_x_0 = const()[name = string("x_491_transpose_x_0"), val = bool(false)]; + bool x_491_transpose_y_0 = const()[name = string("x_491_transpose_y_0"), val = bool(false)]; + tensor var_3859_to_fp16 = const()[name = string("op_3859_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236182400)))]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3857_cast_fp16)[name = string("transpose_158")]; + tensor x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = var_3859_to_fp16)[name = string("x_491_cast_fp16")]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_493_mode_0 = const()[name = string("x_493_mode_0"), val = string("constant")]; + fp16 const_234_to_fp16 = const()[name = string("const_234_to_fp16"), val = fp16(0x0p+0)]; + tensor x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor var_3867 = const()[name = string("op_3867"), val = tensor([1, 8, -1, 188])]; + tensor x_495_cast_fp16 = reshape(shape = var_3867, x = x_493_cast_fp16)[name = string("x_495_cast_fp16")]; + tensor var_3871_begin_0 = const()[name = string("op_3871_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3871_end_0 = const()[name = string("op_3871_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3871_end_mask_0 = const()[name = string("op_3871_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3871_cast_fp16 = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = x_495_cast_fp16)[name = string("op_3871_cast_fp16")]; + tensor var_3872 = const()[name = string("op_3872"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_3872, x = var_3871_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3855_cast_fp16)[name = string("transpose_157")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_3881_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_3881_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_3881_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = string("scores_91_cast_fp16")]; + tensor var_3887_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = string("op_3887_cast_fp16")]; + tensor input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_3887_cast_fp16, cond = mask_3)[name = string("input_1177_cast_fp16")]; + bool x_497_transpose_x_0 = const()[name = string("x_497_transpose_x_0"), val = bool(false)]; + bool x_497_transpose_y_0 = const()[name = string("x_497_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")]; + tensor x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_47_cast_fp16)[name = string("x_497_cast_fp16")]; + tensor var_3891_perm_0 = const()[name = string("op_3891_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3892 = const()[name = string("op_3892"), val = tensor([1, -1, 1024])]; + tensor var_3891_cast_fp16 = transpose(perm = var_3891_perm_0, x = x_497_cast_fp16)[name = string("transpose_154")]; + tensor input_1179_cast_fp16 = reshape(shape = var_3892, x = var_3891_cast_fp16)[name = string("input_1179_cast_fp16")]; + tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236950464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237343744))))[name = string("module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1179_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1183_cast_fp16")]; + tensor x_501_axes_0 = const()[name = string("x_501_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("module_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237360192)))]; + tensor module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("module_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237362304)))]; + tensor x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = string("x_501_cast_fp16")]; + tensor input_1185_perm_0 = const()[name = string("input_1185_perm_0"), val = tensor([0, 2, 1])]; + string input_1187_pad_type_0 = const()[name = string("input_1187_pad_type_0"), val = string("valid")]; + tensor input_1187_strides_0 = const()[name = string("input_1187_strides_0"), val = tensor([1])]; + tensor input_1187_pad_0 = const()[name = string("input_1187_pad_0"), val = tensor([0, 0])]; + tensor input_1187_dilations_0 = const()[name = string("input_1187_dilations_0"), val = tensor([1])]; + int32 input_1187_groups_0 = const()[name = string("input_1187_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237364416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238150912))))[name = string("module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = string("transpose_153")]; + tensor input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1185_cast_fp16)[name = string("input_1187_cast_fp16")]; + int32 x_503_split_num_splits_0 = const()[name = string("x_503_split_num_splits_0"), val = int32(2)]; + int32 x_503_split_axis_0 = const()[name = string("x_503_split_axis_0"), val = int32(1)]; + tensor x_503_split_cast_fp16_0, tensor x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = string("x_503_split_cast_fp16")]; + tensor x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = string("x_503_split_1_sigmoid_cast_fp16")]; + tensor x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_328)[name = string("input_1189_cast_fp16")]; + tensor input_1191_pad_0 = const()[name = string("input_1191_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1191_mode_0 = const()[name = string("input_1191_mode_0"), val = string("constant")]; + fp16 const_237_to_fp16 = const()[name = string("const_237_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = string("input_1191_cast_fp16")]; + string input_1193_pad_type_0 = const()[name = string("input_1193_pad_type_0"), val = string("valid")]; + int32 input_1193_groups_0 = const()[name = string("input_1193_groups_0"), val = int32(1024)]; + tensor input_1193_strides_0 = const()[name = string("input_1193_strides_0"), val = tensor([1])]; + tensor input_1193_pad_0 = const()[name = string("input_1193_pad_0"), val = tensor([0, 0])]; + tensor input_1193_dilations_0 = const()[name = string("input_1193_dilations_0"), val = tensor([1])]; + tensor const_292_to_fp16 = const()[name = string("const_292_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238183744)))]; + tensor const_293_to_fp16 = const()[name = string("const_293_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238202240)))]; + tensor input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16, x = input_1191_cast_fp16)[name = string("input_1195_cast_fp16")]; + tensor input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = string("input_1197_cast_fp16")]; + string x_505_pad_type_0 = const()[name = string("x_505_pad_type_0"), val = string("valid")]; + tensor x_505_strides_0 = const()[name = string("x_505_strides_0"), val = tensor([1])]; + tensor x_505_pad_0 = const()[name = string("x_505_pad_0"), val = tensor([0, 0])]; + tensor x_505_dilations_0 = const()[name = string("x_505_dilations_0"), val = tensor([1])]; + int32 x_505_groups_0 = const()[name = string("x_505_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238204352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238597632))))[name = string("module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1197_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = string("transpose_152")]; + tensor input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = string("input_1201_cast_fp16")]; + tensor input_1203_axes_0 = const()[name = string("input_1203_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238614080)))]; + tensor module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238616192)))]; + tensor input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238618304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240191232))))[name = string("module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1203_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240256832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241829760))))[name = string("module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1207_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_3952_to_fp16 = const()[name = string("op_3952_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3953_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3952_to_fp16)[name = string("op_3953_cast_fp16")]; + tensor input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3953_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor input_1215_axes_0 = const()[name = string("input_1215_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_out_weight_to_fp16 = const()[name = string("module_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241846208)))]; + tensor module_layers_22_norm_out_bias_to_fp16 = const()[name = string("module_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241848320)))]; + tensor input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = string("input_1215_cast_fp16")]; + tensor input_1217_axes_0 = const()[name = string("input_1217_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241850432)))]; + tensor module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241852544)))]; + tensor input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = string("input_1217_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241854656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243427584))))[name = string("module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1217_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243493184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245066112))))[name = string("module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1221_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_3981_to_fp16 = const()[name = string("op_3981_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3982_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3981_to_fp16)[name = string("op_3982_cast_fp16")]; + tensor input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3982_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor query_axes_0 = const()[name = string("query_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245082560)))]; + tensor module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245084672)))]; + tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = string("query_cast_fp16")]; + tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245086784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245480064))))[name = string("module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_3998 = const()[name = string("op_3998"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_3998, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245496512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245889792))))[name = string("module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_4002 = const()[name = string("op_4002"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_4002, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245906240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246299520))))[name = string("module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_4006 = const()[name = string("op_4006"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_4006, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246315968)))]; + tensor var_4018_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4018_cast_fp16")]; + tensor module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246318080)))]; + tensor var_4020_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4020_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_513_transpose_x_0 = const()[name = string("x_513_transpose_x_0"), val = bool(false)]; + bool x_513_transpose_y_0 = const()[name = string("x_513_transpose_y_0"), val = bool(false)]; + tensor var_4022_to_fp16 = const()[name = string("op_4022_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246320192)))]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4020_cast_fp16)[name = string("transpose_151")]; + tensor x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_4022_to_fp16)[name = string("x_513_cast_fp16")]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_515_mode_0 = const()[name = string("x_515_mode_0"), val = string("constant")]; + fp16 const_244_to_fp16 = const()[name = string("const_244_to_fp16"), val = fp16(0x0p+0)]; + tensor x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor var_4030 = const()[name = string("op_4030"), val = tensor([1, 8, -1, 188])]; + tensor x_517_cast_fp16 = reshape(shape = var_4030, x = x_515_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor var_4034_begin_0 = const()[name = string("op_4034_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4034_end_0 = const()[name = string("op_4034_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_4034_end_mask_0 = const()[name = string("op_4034_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4034_cast_fp16 = slice_by_index(begin = var_4034_begin_0, end = var_4034_end_0, end_mask = var_4034_end_mask_0, x = x_517_cast_fp16)[name = string("op_4034_cast_fp16")]; + tensor var_4035 = const()[name = string("op_4035"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4035, x = var_4034_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4018_cast_fp16)[name = string("transpose_150")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_4044_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4044_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_4044_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = string("scores_cast_fp16")]; + tensor var_4050_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_4050_cast_fp16")]; + tensor input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4050_cast_fp16, cond = mask_3)[name = string("input_1229_cast_fp16")]; + bool x_519_transpose_x_0 = const()[name = string("x_519_transpose_x_0"), val = bool(false)]; + bool x_519_transpose_y_0 = const()[name = string("x_519_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")]; + tensor x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor var_4054_perm_0 = const()[name = string("op_4054_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4055 = const()[name = string("op_4055"), val = tensor([1, -1, 1024])]; + tensor var_4054_cast_fp16 = transpose(perm = var_4054_perm_0, x = x_519_cast_fp16)[name = string("transpose_147")]; + tensor input_1231_cast_fp16 = reshape(shape = var_4055, x = var_4054_cast_fp16)[name = string("input_1231_cast_fp16")]; + tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247088256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247481536))))[name = string("module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1231_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1235_cast_fp16")]; + tensor x_523_axes_0 = const()[name = string("x_523_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("module_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247497984)))]; + tensor module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("module_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247500096)))]; + tensor x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = string("x_523_cast_fp16")]; + tensor input_1237_perm_0 = const()[name = string("input_1237_perm_0"), val = tensor([0, 2, 1])]; + string input_1239_pad_type_0 = const()[name = string("input_1239_pad_type_0"), val = string("valid")]; + tensor input_1239_strides_0 = const()[name = string("input_1239_strides_0"), val = tensor([1])]; + tensor input_1239_pad_0 = const()[name = string("input_1239_pad_0"), val = tensor([0, 0])]; + tensor input_1239_dilations_0 = const()[name = string("input_1239_dilations_0"), val = tensor([1])]; + int32 input_1239_groups_0 = const()[name = string("input_1239_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247502208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248288704))))[name = string("module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = string("transpose_146")]; + tensor input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1237_cast_fp16)[name = string("input_1239_cast_fp16")]; + int32 x_525_split_num_splits_0 = const()[name = string("x_525_split_num_splits_0"), val = int32(2)]; + int32 x_525_split_axis_0 = const()[name = string("x_525_split_axis_0"), val = int32(1)]; + tensor x_525_split_cast_fp16_0, tensor x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = string("x_525_split_cast_fp16")]; + tensor x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = string("x_525_split_1_sigmoid_cast_fp16")]; + tensor x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = string("x_525_cast_fp16")]; + tensor input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_328)[name = string("input_1241_cast_fp16")]; + tensor input_1243_pad_0 = const()[name = string("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1243_mode_0 = const()[name = string("input_1243_mode_0"), val = string("constant")]; + fp16 const_247_to_fp16 = const()[name = string("const_247_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = string("input_1243_cast_fp16")]; + string input_1245_pad_type_0 = const()[name = string("input_1245_pad_type_0"), val = string("valid")]; + int32 input_1245_groups_0 = const()[name = string("input_1245_groups_0"), val = int32(1024)]; + tensor input_1245_strides_0 = const()[name = string("input_1245_strides_0"), val = tensor([1])]; + tensor input_1245_pad_0 = const()[name = string("input_1245_pad_0"), val = tensor([0, 0])]; + tensor input_1245_dilations_0 = const()[name = string("input_1245_dilations_0"), val = tensor([1])]; + tensor const_294_to_fp16 = const()[name = string("const_294_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248321536)))]; + tensor const_295_to_fp16 = const()[name = string("const_295_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248340032)))]; + tensor input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16, x = input_1243_cast_fp16)[name = string("input_1247_cast_fp16")]; + tensor input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = string("input_1249_cast_fp16")]; + string x_527_pad_type_0 = const()[name = string("x_527_pad_type_0"), val = string("valid")]; + tensor x_527_strides_0 = const()[name = string("x_527_strides_0"), val = tensor([1])]; + tensor x_527_pad_0 = const()[name = string("x_527_pad_0"), val = tensor([0, 0])]; + tensor x_527_dilations_0 = const()[name = string("x_527_dilations_0"), val = tensor([1])]; + int32 x_527_groups_0 = const()[name = string("x_527_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248342144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248735424))))[name = string("module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1249_cast_fp16)[name = string("x_527_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = string("transpose_145")]; + tensor input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = string("input_1253_cast_fp16")]; + tensor input_1255_axes_0 = const()[name = string("input_1255_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248751872)))]; + tensor module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248753984)))]; + tensor input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248756096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250329024))))[name = string("module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1255_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250394624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251967552))))[name = string("module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1259_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_4115_to_fp16 = const()[name = string("op_4115_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4116_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4115_to_fp16)[name = string("op_4116_cast_fp16")]; + tensor input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4116_cast_fp16)[name = string("input_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_out_weight_to_fp16 = const()[name = string("module_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251984000)))]; + tensor module_layers_23_norm_out_bias_to_fp16 = const()[name = string("module_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251986112)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")]; + tensor obj_1_perm_0 = const()[name = string("obj_1_perm_0"), val = tensor([0, 2, 1])]; + string obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")]; + tensor encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = string("cast_0")]; + } -> (encoder, encoder_length); +} \ No newline at end of file diff --git a/bark-3.0-clean/Encoder_lut3.mlmodelc/weights/weight.bin b/bark-3.0-clean/Encoder_lut3.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..dbab23edd49b68c825af7cbb076a63a0e4e0190d --- /dev/null +++ b/bark-3.0-clean/Encoder_lut3.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a80e996f0ff2eda1a63c30dd0276d0e1ba4bcb9352ce293df8bc5d1143b73b36 +size 251988224 diff --git a/bark-3.0-clean/JointDecisionv3.mlmodelc/analytics/coremldata.bin b/bark-3.0-clean/JointDecisionv3.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cfcb9746ee27752a786802c99d2c21df63df89ea --- /dev/null +++ b/bark-3.0-clean/JointDecisionv3.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a55f011ca119fc7913ffc905ef48079572c4f54b6e5ed34a033f69c5422be0b6 +size 243 diff --git a/bark-3.0-clean/JointDecisionv3.mlmodelc/coremldata.bin b/bark-3.0-clean/JointDecisionv3.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5052161053b85dd58798516eed362f9d60545243 --- /dev/null +++ b/bark-3.0-clean/JointDecisionv3.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f5d97969689b6397b8f2e8ba31a1d4684a41ec373ecf56d4ee751efb2ca5073 +size 601 diff --git a/bark-3.0-clean/JointDecisionv3.mlmodelc/metadata.json b/bark-3.0-clean/JointDecisionv3.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c8b8b30d11b8cac5fd029f8129c773023cd78dab --- /dev/null +++ b/bark-3.0-clean/JointDecisionv3.mlmodelc/metadata.json @@ -0,0 +1,124 @@ +[ + { + "metadataOutputVersion" : "3.0", + "shortDescription" : "Underdog Bark 3.0 Clean single-step joint decision (top-K 64)", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1, 1]", + "name" : "token_id", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1, 1]", + "name" : "token_prob", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1, 1]", + "name" : "duration", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1 × 1 × 64)", + "shortDescription" : "", + "shape" : "[1, 1, 1, 64]", + "name" : "top_k_ids", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1 × 1 × 64)", + "shortDescription" : "", + "shape" : "[1, 1, 1, 64]", + "name" : "top_k_logits", + "type" : "MultiArray" + } + ], + "storagePrecision" : "Float16", + "modelParameters" : [ + + ], + "author" : "Fluid Inference", + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Ios17.reduceArgmax" : 2, + "Ios17.linear" : 3, + "Ios17.transpose" : 2, + "Ios17.sliceByIndex" : 2, + "Ios17.add" : 1, + "Ios17.topk" : 1, + "Ios16.relu" : 1, + "Ios16.softmax" : 1, + "Ios17.expandDims" : 3, + "Ios17.squeeze" : 1, + "Ios17.cast" : 6, + "Ios17.gatherAlongAxis" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1024 × 1)", + "shortDescription" : "", + "shape" : "[1, 1024, 1]", + "name" : "encoder_step", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 640 × 1)", + "shortDescription" : "", + "shape" : "[1, 640, 1]", + "name" : "decoder_step", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-10-21", + "com.github.apple.coremltools.source" : "torch==2.7.0", + "com.github.apple.coremltools.version" : "9.0b1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "JointDecisionv3", + "method" : "predict" + } +] \ No newline at end of file diff --git a/bark-3.0-clean/JointDecisionv3.mlmodelc/model.mil b/bark-3.0-clean/JointDecisionv3.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..322a3748e073a27aefd42984cfa25e26bb52b63f --- /dev/null +++ b/bark-3.0-clean/JointDecisionv3.mlmodelc/model.mil @@ -0,0 +1,69 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] +{ + func main(tensor decoder_step, tensor encoder_step) { + tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; + tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; + tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1310848)))]; + tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_5")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312192)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2131456)))]; + tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_4")]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; + tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; + tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; + tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; + tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2132800)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12626304)))]; + tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; + tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 8193])]; + tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; + tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; + tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 8193])]; + tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 8198])]; + tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; + tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; + tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; + tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; + tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; + tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; + tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; + tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; + tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; + tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; + tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; + tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; + tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_3")]; + tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; + tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; + tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; + tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; + tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; + tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; + tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; + tensor var_72 = const()[name = tensor("op_72"), val = tensor(64)]; + tensor var_76_axis_0 = const()[name = tensor("op_76_axis_0"), val = tensor(-1)]; + tensor var_76_ascending_0 = const()[name = tensor("op_76_ascending_0"), val = tensor(false)]; + tensor var_76_sort_0 = const()[name = tensor("op_76_sort_0"), val = tensor(true)]; + tensor var_76_return_indices_0 = const()[name = tensor("op_76_return_indices_0"), val = tensor(true)]; + tensor var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor("uint16")]; + tensor var_76_cast_fp16_cast_int16_0, tensor var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor("op_76_cast_fp16_cast_int16")]; + tensor var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor("int32")]; + tensor var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor("cast_0")]; + tensor top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor("cast_1")]; + tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_2")]; + } -> (token_id, token_prob, duration, top_k_ids, top_k_logits); +} \ No newline at end of file diff --git a/bark-3.0-clean/JointDecisionv3.mlmodelc/weights/weight.bin b/bark-3.0-clean/JointDecisionv3.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..f36624de2e8caa50ca53ec05e5e522fd24dc1798 --- /dev/null +++ b/bark-3.0-clean/JointDecisionv3.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4575e6ec06917259358cf17307fe7aa38d364b736e4b6c9b6e3d49169c760eee +size 12642764 diff --git a/bark-3.0-clean/NOTICE b/bark-3.0-clean/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..e04476c23fbce7fc6dc485982705328a940dac78 --- /dev/null +++ b/bark-3.0-clean/NOTICE @@ -0,0 +1,13 @@ +Underdog Bark 3.0 Clean (ConwayResearch/Underdog-Bark-3.0-Clean), part of the Bark 3.0 family + +This model derives from: + + nvidia/parakeet-tdt-0.6b-v3, (c) NVIDIA Corporation, CC-BY-4.0 + https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 + moondream/parakeet-ultra, (c) Moondream, CC-BY-4.0 + https://huggingface.co/moondream/parakeet-ultra + +License: https://creativecommons.org/licenses/by/4.0/ + +Changes: post-trained and compressed by Conway Research. The FastConformer encoder architecture, TDT decoder, +tokenizer and feature-extractor configuration derive from the models above and remain under CC-BY-4.0. diff --git a/bark-3.0-clean/Preprocessor.mlmodelc/analytics/coremldata.bin b/bark-3.0-clean/Preprocessor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cadf776b6eddc28eae76654cdff21b7c61efbd78 --- /dev/null +++ b/bark-3.0-clean/Preprocessor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9beeb989c8d66f8be11df59bc6df277ec76cee404f6865b46243835ef562f6d +size 243 diff --git a/bark-3.0-clean/Preprocessor.mlmodelc/coremldata.bin b/bark-3.0-clean/Preprocessor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c86b363f1bbf6a4f38ec5757390b7ac17e99adda --- /dev/null +++ b/bark-3.0-clean/Preprocessor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dbde3f2300842c1fd51ef3ff948a0bcffe65ffd2dca10707f2509f32c1d65b1d +size 486 diff --git a/bark-3.0-clean/Preprocessor.mlmodelc/metadata.json b/bark-3.0-clean/Preprocessor.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..06162c58cf2f5343a0e60f36f843bcdf3cc58527 --- /dev/null +++ b/bark-3.0-clean/Preprocessor.mlmodelc/metadata.json @@ -0,0 +1,105 @@ +[ + { + "metadataOutputVersion" : "3.0", + "shortDescription" : "int8-linear quantized - preprocessor", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 128 × 1501)", + "shortDescription" : "", + "shape" : "[1, 128, 1501]", + "name" : "mel", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "mel_length", + "type" : "MultiArray" + } + ], + "storagePrecision" : "Int8", + "modelParameters" : [ + + ], + "author" : "Fluid Inference", + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Ios17.reshape" : 2, + "Ios17.matmul" : 1, + "Select" : 3, + "Ios17.expandDims" : 8, + "Ios17.add" : 4, + "Tile" : 1, + "Ios17.sliceByIndex" : 3, + "Ios16.reduceSum" : 4, + "Pad" : 1, + "Ios17.log" : 1, + "Ios16.constexprAffineDequantize" : 4, + "Ios17.less" : 1, + "Ios17.sub" : 4, + "Ios17.conv" : 2, + "Ios17.pow" : 2, + "Ios17.cast" : 5, + "Stack" : 1, + "Ios17.concat" : 1, + "Ios17.floorDiv" : 1, + "Ios17.realDiv" : 3, + "Ios17.sqrt" : 1, + "Ios17.greaterEqual" : 1, + "Ios17.mul" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 240000)", + "shortDescription" : "", + "shape" : "[1, 240000]", + "name" : "audio_signal", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "audio_length", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2025-09-19", + "com.github.apple.coremltools.source" : "torch==2.7.0", + "com.github.apple.coremltools.version" : "9.0b1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "parakeet_preprocessor", + "method" : "predict" + } +] \ No newline at end of file diff --git a/bark-3.0-clean/Preprocessor.mlmodelc/model.mil b/bark-3.0-clean/Preprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..f987e2479228436c49189aa47edb376db74dcff5 --- /dev/null +++ b/bark-3.0-clean/Preprocessor.mlmodelc/model.mil @@ -0,0 +1,125 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] +{ + func main(tensor audio_length, tensor audio_signal) { + tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; + tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; + tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; + tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; + tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; + tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; + tensor var_38_to_fp16_dtype_0 = const()[name = tensor("op_38_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor var_39_promoted_to_fp16 = const()[name = tensor("op_39_promoted_to_fp16"), val = tensor(0x1p+0)]; + tensor floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor("cast_4")]; + tensor seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor("seq_len_1_cast_fp16")]; + tensor seq_len_dtype_0 = const()[name = tensor("seq_len_dtype_0"), val = tensor("int32")]; + tensor var_43_begin_0 = const()[name = tensor("op_43_begin_0"), val = tensor([0, 0])]; + tensor var_43_end_0 = const()[name = tensor("op_43_end_0"), val = tensor([1, 1])]; + tensor var_43_end_mask_0 = const()[name = tensor("op_43_end_mask_0"), val = tensor([true, false])]; + tensor var_43_squeeze_mask_0 = const()[name = tensor("op_43_squeeze_mask_0"), val = tensor([false, true])]; + tensor audio_signal_to_fp16_dtype_0 = const()[name = tensor("audio_signal_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor("cast_3")]; + tensor var_43_cast_fp16 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor("op_43_cast_fp16")]; + tensor var_44_axes_0 = const()[name = tensor("op_44_axes_0"), val = tensor([1])]; + tensor var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor("op_44_cast_fp16")]; + tensor var_46_begin_0 = const()[name = tensor("op_46_begin_0"), val = tensor([0, 1])]; + tensor var_46_end_0 = const()[name = tensor("op_46_end_0"), val = tensor([1, 240000])]; + tensor var_46_end_mask_0 = const()[name = tensor("op_46_end_mask_0"), val = tensor([true, true])]; + tensor var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_46_cast_fp16")]; + tensor var_48_begin_0 = const()[name = tensor("op_48_begin_0"), val = tensor([0, 0])]; + tensor var_48_end_0 = const()[name = tensor("op_48_end_0"), val = tensor([1, 239999])]; + tensor var_48_end_mask_0 = const()[name = tensor("op_48_end_mask_0"), val = tensor([true, false])]; + tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_48_cast_fp16")]; + tensor var_49_to_fp16 = const()[name = tensor("op_49_to_fp16"), val = tensor(0x1.f0cp-1)]; + tensor var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor("op_50_cast_fp16")]; + tensor var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor("op_51_cast_fp16")]; + tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; + tensor input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_cast_fp16))[name = tensor("input_1_cast_fp16")]; + tensor var_57 = const()[name = tensor("op_57"), val = tensor([1, 1, 240000])]; + tensor input_3_cast_fp16 = reshape(shape = var_57, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; + tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("reflect")]; + tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_63 = const()[name = tensor("op_63"), val = tensor([1, 240512])]; + tensor input_cast_fp16 = reshape(shape = var_63, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor expand_dims_5 = const()[name = tensor("expand_dims_5"), val = tensor([160])]; + tensor expand_dims_6_axes_0 = const()[name = tensor("expand_dims_6_axes_0"), val = tensor([1])]; + tensor expand_dims_6_cast_fp16 = expand_dims(axes = expand_dims_6_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_6_cast_fp16")]; + tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; + tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; + tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; + tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; + tensor expand_dims_3_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_3_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132096))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131712)))]; + tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_5, weight = expand_dims_3_to_fp16_quantized, x = expand_dims_6_cast_fp16)[name = tensor("conv_0_cast_fp16")]; + tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; + tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; + tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; + tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; + tensor expand_dims_4_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_4_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132736))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264768))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264384)))]; + tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_5, weight = expand_dims_4_to_fp16_quantized, x = expand_dims_6_cast_fp16)[name = tensor("conv_1_cast_fp16")]; + tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; + tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; + tensor var_17_promoted_to_fp16 = const()[name = tensor("op_17_promoted_to_fp16"), val = tensor(0x1p+1)]; + tensor var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor("op_67_cast_fp16")]; + tensor var_69_axes_0 = const()[name = tensor("op_69_axes_0"), val = tensor([-1])]; + tensor var_69_keep_dims_0 = const()[name = tensor("op_69_keep_dims_0"), val = tensor(false)]; + tensor var_69_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor("op_69_cast_fp16")]; + tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; + tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; + tensor const_6_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(1), name = tensor("const_6_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265408))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298560))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298368)))]; + tensor x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_6_to_fp16_quantized, y = var_69_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor var_76_to_fp16 = const()[name = tensor("op_76_to_fp16"), val = tensor(0x1p-24)]; + tensor var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor("op_77_cast_fp16")]; + tensor x_13_epsilon_0 = const()[name = tensor("x_13_epsilon_0"), val = tensor(0x1p-149)]; + tensor x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor var_82 = const()[name = tensor("op_82"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])]; + tensor var_85_axes_0 = const()[name = tensor("op_85_axes_0"), val = tensor([1])]; + tensor mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor("cast_2")]; + tensor var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor("op_85")]; + tensor valid_mask = less(x = var_82, y = var_85)[name = tensor("valid_mask")]; + tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; + tensor var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor("op_87")]; + tensor var_87_after_broadcast_reps_0 = const()[name = tensor("op_87_after_broadcast_reps_0"), val = tensor([1, 128, 1])]; + tensor var_87_after_broadcast = tile(reps = var_87_after_broadcast_reps_0, x = var_87)[name = tensor("op_87_after_broadcast")]; + tensor op_24_after_broadcast_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("op_24_after_broadcast_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298880))), scale = tensor(0x0p+0), zero_point = tensor(0)]; + tensor var_88_cast_fp16 = select(a = x_13_cast_fp16, b = op_24_after_broadcast_to_fp16_quantized, cond = var_87_after_broadcast)[name = tensor("op_88_cast_fp16")]; + tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; + tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; + tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; + tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; + tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; + tensor cast_2_to_fp16_dtype_0 = const()[name = tensor("cast_2_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_1")]; + tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; + tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; + tensor var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_93_cast_fp16")]; + tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor("x_mean_cast_fp16")]; + tensor var_96_axes_0 = const()[name = tensor("op_96_axes_0"), val = tensor([2])]; + tensor var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor("op_96_cast_fp16")]; + tensor var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = select(a = var_97_cast_fp16, b = op_24_after_broadcast_to_fp16_quantized, cond = var_87_after_broadcast)[name = tensor("op_98_cast_fp16")]; + tensor var_17_promoted_1_to_fp16 = const()[name = tensor("op_17_promoted_1_to_fp16"), val = tensor(0x1p+1)]; + tensor var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor("op_99_cast_fp16")]; + tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([2])]; + tensor var_101_keep_dims_0 = const()[name = tensor("op_101_keep_dims_0"), val = tensor(false)]; + tensor var_101_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor("op_101_cast_fp16")]; + tensor var_103_to_fp16 = const()[name = tensor("op_103_to_fp16"), val = tensor(0x1p+0)]; + tensor var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor("op_104_cast_fp16")]; + tensor var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; + tensor x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; + tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; + tensor x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; + tensor var_110_axes_0 = const()[name = tensor("op_110_axes_0"), val = tensor([2])]; + tensor var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor("op_110_cast_fp16")]; + tensor x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor mask = greater_equal(x = var_82, y = var_85)[name = tensor("mask")]; + tensor var_119_axes_0 = const()[name = tensor("op_119_axes_0"), val = tensor([1])]; + tensor var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor("op_119")]; + tensor var_24_to_fp16 = const()[name = tensor("op_24_to_fp16"), val = tensor(0x0p+0)]; + tensor processed_signal_cast_fp16 = select(a = var_24_to_fp16, b = x_cast_fp16, cond = var_119)[name = tensor("processed_signal_cast_fp16")]; + tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_0")]; + } -> (mel, mel_length); +} \ No newline at end of file diff --git a/bark-3.0-clean/Preprocessor.mlmodelc/weights/weight.bin b/bark-3.0-clean/Preprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..47be051f50981bc166d0bcf6111cff47dd0db117 --- /dev/null +++ b/bark-3.0-clean/Preprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:129b76e3aeafa8afa3ea76d995b964b145fe83700d579f6ff42c4c38fa0968ea +size 491072 diff --git a/bark-3.0-clean/parakeet_vocab.json b/bark-3.0-clean/parakeet_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..c684822ec3b671ebc449c433a7012398d39b8bb0 --- /dev/null +++ b/bark-3.0-clean/parakeet_vocab.json @@ -0,0 +1,8194 @@ +{ + "0": "", + "1": "<|nospeech|>", + "2": "", + "3": "<|endoftext|>", + "4": "<|startoftranscript|>", + "5": "<|pnc|>", + "6": "<|nopnc|>", + "7": "<|startofcontext|>", + "8": "<|itn|>", + "9": "<|noitn|>", + "10": "<|timestamp|>", + "11": "<|notimestamp|>", + "12": "<|diarize|>", + "13": "<|nodiarize|>", + "14": "<|spkchange|>", + "15": "<|audioseparator|>", + "16": "<|emo:undefined|>", + "17": "<|emo:neutral|>", + "18": "<|emo:happy|>", + "19": "<|emo:sad|>", + "20": "<|emo:angry|>", + "21": "<|unklang|>", + "22": "<|predict_lang|>", + "23": "<|nopredict_lang|>", + "24": "<|aa|>", + "25": "<|ab|>", + "26": "<|af|>", + "27": "<|ak|>", + "28": "<|sq|>", + "29": "<|am|>", + "30": "<|ar|>", + "31": "<|an|>", + "32": "<|hy|>", + "33": "<|as|>", + "34": "<|av|>", + "35": "<|ae|>", + "36": "<|ay|>", + "37": "<|az|>", + "38": "<|bm|>", + "39": "<|ba|>", + "40": "<|eu|>", + "41": "<|be|>", + "42": "<|bn|>", + "43": "<|bi|>", + "44": "<|bs|>", + "45": "<|br|>", + "46": "<|bg|>", + "47": "<|my|>", + "48": "<|ca|>", + "49": "<|ch|>", + "50": "<|ce|>", + "51": "<|ny|>", + "52": "<|zh|>", + "53": "<|cu|>", + "54": "<|cv|>", + "55": "<|kw|>", + "56": "<|co|>", + "57": "<|cr|>", + "58": "<|hr|>", + "59": "<|cs|>", + "60": "<|da|>", + "61": "<|dv|>", + "62": "<|nl|>", + "63": "<|dz|>", + "64": "<|en|>", + "65": "<|eo|>", + "66": "<|et|>", + "67": "<|ee|>", + "68": "<|fo|>", + "69": "<|fj|>", + "70": "<|fi|>", + "71": "<|fr|>", + "72": "<|fy|>", + "73": "<|ff|>", + "74": "<|gd|>", + "75": "<|gl|>", + "76": "<|lg|>", + "77": "<|ka|>", + "78": "<|de|>", + "79": "<|el|>", + "80": "<|kl|>", + "81": "<|gn|>", + "82": "<|gu|>", + "83": "<|ht|>", + "84": "<|ha|>", + "85": "<|he|>", + "86": "<|hz|>", + "87": "<|hi|>", + "88": "<|ho|>", + "89": "<|hu|>", + "90": "<|is|>", + "91": "<|io|>", + "92": "<|ig|>", + "93": "<|id|>", + "94": "<|ia|>", + "95": "<|ie|>", + "96": "<|iu|>", + "97": "<|ik|>", + "98": "<|ga|>", + "99": "<|it|>", + "100": "<|ja|>", + "101": "<|jv|>", + "102": "<|kn|>", + "103": "<|kr|>", + "104": "<|ks|>", + "105": "<|kk|>", + "106": "<|km|>", + "107": "<|ki|>", + "108": "<|rw|>", + "109": "<|ky|>", + "110": "<|kv|>", + "111": "<|kg|>", + "112": "<|ko|>", + "113": "<|kj|>", + "114": "<|ku|>", + "115": "<|lo|>", + "116": "<|la|>", + "117": "<|lv|>", + "118": "<|li|>", + "119": "<|ln|>", + "120": "<|lt|>", + "121": "<|lu|>", + "122": "<|lb|>", + "123": "<|mk|>", + "124": "<|mg|>", + "125": "<|ms|>", + "126": "<|ml|>", + "127": "<|mt|>", + "128": "<|gv|>", + "129": "<|mi|>", + "130": "<|mr|>", + "131": "<|mh|>", + "132": "<|mn|>", + "133": "<|na|>", + "134": "<|nv|>", + "135": "<|nd|>", + "136": "<|nr|>", + "137": "<|ng|>", + "138": "<|ne|>", + "139": "<|no|>", + "140": "<|nb|>", + "141": "<|nn|>", + "142": "<|oc|>", + "143": "<|oj|>", + "144": "<|or|>", + "145": "<|om|>", + "146": "<|os|>", + "147": "<|pi|>", + "148": "<|ps|>", + "149": "<|fa|>", + "150": "<|pl|>", + "151": "<|pt|>", + "152": "<|pa|>", + "153": "<|qu|>", + "154": "<|ro|>", + "155": "<|rm|>", + "156": "<|rn|>", + "157": "<|ru|>", + "158": "<|se|>", + "159": "<|sm|>", + "160": "<|sg|>", + "161": "<|sa|>", + "162": "<|sc|>", + "163": "<|sr|>", + "164": "<|sn|>", + "165": "<|sd|>", + "166": "<|si|>", + "167": "<|sk|>", + "168": "<|sl|>", + "169": "<|so|>", + "170": "<|st|>", + "171": "<|es|>", + "172": "<|su|>", + "173": "<|sw|>", + "174": "<|ss|>", + "175": "<|sv|>", + "176": "<|tl|>", + "177": "<|ty|>", + "178": "<|tg|>", + "179": "<|ta|>", + "180": "<|tt|>", + "181": "<|te|>", + "182": "<|th|>", + "183": "<|bo|>", + "184": "<|ti|>", + "185": "<|to|>", + "186": "<|ts|>", + "187": "<|tn|>", + "188": "<|tr|>", + "189": "<|tk|>", + "190": "<|tw|>", + "191": "<|ug|>", + "192": "<|uk|>", + "193": "<|ur|>", + "194": "<|uz|>", + "195": "<|ve|>", + "196": "<|vi|>", + "197": "<|vo|>", + "198": "<|wa|>", + "199": "<|cy|>", + "200": "<|wo|>", + "201": "<|xh|>", + "202": "<|ii|>", + "203": "<|yi|>", + "204": "<|yo|>", + "205": "<|za|>", + "206": "<|zu|>", + "207": "<|0|>", + "208": "<|1|>", + "209": "<|2|>", + "210": "<|3|>", + "211": "<|4|>", + "212": "<|5|>", + "213": "<|6|>", + "214": "<|7|>", + "215": "<|8|>", + "216": "<|9|>", + "217": "<|10|>", + "218": "<|spk0|>", + "219": "<|spk1|>", + "220": "<|spk2|>", + "221": "<|spk3|>", + "222": "<|spk4|>", + "223": "<|spk5|>", + "224": "<|spk6|>", + "225": "<|spk7|>", + "226": "<|spk8|>", + "227": "<|spk9|>", + "228": "<|spk10|>", + "229": "<|spk11|>", + "230": "<|spk12|>", + "231": "<|spk13|>", + "232": "<|spk14|>", + "233": "<|spk15|>", + "234": "0", + "235": "1", + "236": "2", + "237": "3", + "238": "4", + "239": "5", + "240": "6", + "241": "7", + "242": "8", + "243": "9", + "244": "<|spltoken0|>", + "245": "<|spltoken1|>", + "246": "<|spltoken2|>", + "247": "<|spltoken3|>", + "248": "<|spltoken4|>", + "249": "<|spltoken5|>", + "250": "<|spltoken6|>", + "251": "<|spltoken7|>", + "252": "<|spltoken8|>", + "253": "<|spltoken9|>", + "254": "<|spltoken10|>", + "255": "<|spltoken11|>", + "256": "<|spltoken12|>", + "257": "<|spltoken13|>", + "258": "<|spltoken14|>", + "259": "<|spltoken15|>", + "260": "<|spltoken16|>", + "261": "<|spltoken17|>", + "262": "<|spltoken18|>", + "263": "<|spltoken19|>", + "264": "<|spltoken20|>", + "265": "<|spltoken21|>", + "266": "<|spltoken22|>", + "267": "<|spltoken23|>", + "268": "<|spltoken24|>", + "269": "<|spltoken25|>", + "270": "<|spltoken26|>", + "271": "<|spltoken27|>", + "272": "<|spltoken28|>", + "273": "<|spltoken29|>", + "274": "en", + "275": " s", + "276": " d", + "277": "er", + "278": " p", + "279": " a", + "280": " t", + "281": "in", + "282": " m", + "283": "es", + "284": "an", + "285": " n", + "286": " v", + "287": "ar", + "288": " k", + "289": "on", + "290": "is", + "291": "et", + "292": "el", + "293": " l", + "294": "or", + "295": "al", + "296": "at", + "297": "as", + "298": " c", + "299": " o", + "300": " j", + "301": "it", + "302": " b", + "303": " h", + "304": "ie", + "305": "il", + "306": "re", + "307": " u", + "308": "os", + "309": " f", + "310": "ch", + "311": "em", + "312": " i", + "313": "om", + "314": "ra", + "315": "te", + "316": "am", + "317": " g", + "318": " de", + "319": "ad", + "320": "ro", + "321": "ti", + "322": " z", + "323": "us", + "324": "ol", + "325": " e", + "326": "ed", + "327": "ta", + "328": " w", + "329": " п", + "330": "ri", + "331": "un", + "332": "li", + "333": " н", + "334": " in", + "335": "id", + "336": " с", + "337": "le", + "338": " в", + "339": "ak", + "340": "ent", + "341": "qu", + "342": "то", + "343": " S", + "344": "ur", + "345": "ik", + "346": " A", + "347": " se", + "348": "im", + "349": "od", + "350": "ci", + "351": "ut", + "352": " un", + "353": "ov", + "354": " je", + "355": "ul", + "356": " M", + "357": " T", + "358": " na", + "359": "ot", + "360": " D", + "361": " д", + "362": " E", + "363": " qu", + "364": " en", + "365": "est", + "366": " to", + "367": "eg", + "368": "st", + "369": "ai", + "370": "ag", + "371": "ij", + "372": "ра", + "373": " P", + "374": "та", + "375": " r", + "376": "ud", + "377": "ek", + "378": "ig", + "379": " τ", + "380": " I", + "381": "ing", + "382": " N", + "383": "ни", + "384": "av", + "385": "um", + "386": "ко", + "387": "ia", + "388": "he", + "389": "те", + "390": "но", + "391": " на", + "392": "ва", + "393": " la", + "394": "ро", + "395": " з", + "396": "og", + "397": " о", + "398": "ist", + "399": "ir", + "400": " м", + "401": "de", + "402": " и", + "403": "la", + "404": "ic", + "405": " st", + "406": " J", + "407": " at", + "408": " po", + "409": " ne", + "410": " pr", + "411": " an", + "412": "ли", + "413": " al", + "414": "aj", + "415": "ти", + "416": "iv", + "417": " C", + "418": "ре", + "419": "je", + "420": " по", + "421": "ка", + "422": "на", + "423": " es", + "424": "and", + "425": " H", + "426": " B", + "427": " re", + "428": "ου", + "429": "ok", + "430": " K", + "431": " on", + "432": "ne", + "433": " pro", + "434": "to", + "435": " б", + "436": "ab", + "437": " π", + "438": "ou", + "439": " L", + "440": "го", + "441": "ter", + "442": "ien", + "443": " que", + "444": " V", + "445": " da", + "446": "ja", + "447": "na", + "448": "ka", + "449": "ри", + "450": " par", + "451": " su", + "452": " O", + "453": " et", + "454": "ion", + "455": " ja", + "456": "να", + "457": " α", + "458": " do", + "459": " y", + "460": " el", + "461": "ru", + "462": " é", + "463": "ce", + "464": " κ", + "465": " di", + "466": " σ", + "467": "se", + "468": " er", + "469": "ст", + "470": "ma", + "471": " con", + "472": " ε", + "473": "no", + "474": "ap", + "475": " si", + "476": "ен", + "477": "me", + "478": " te", + "479": "ni", + "480": " ч", + "481": " le", + "482": " μ", + "483": " ma", + "484": "än", + "485": " sp", + "486": " за", + "487": "ost", + "488": "end", + "489": "ko", + "490": "are", + "491": " com", + "492": "ke", + "493": " no", + "494": "är", + "495": "ла", + "496": "ers", + "497": " G", + "498": " sa", + "499": " W", + "500": " ir", + "501": "ere", + "502": " vi", + "503": " li", + "504": " не", + "505": " ž", + "506": " the", + "507": "ve", + "508": " is", + "509": " for", + "510": "ob", + "511": "ле", + "512": "во", + "513": "si", + "514": "αι", + "515": " F", + "516": " il", + "517": " U", + "518": " R", + "519": "ть", + "520": "ali", + "521": "ine", + "522": "sa", + "523": " ко", + "524": " š", + "525": " me", + "526": "va", + "527": "ant", + "528": "vo", + "529": "ει", + "530": "ell", + "531": " vo", + "532": "ill", + "533": " det", + "534": "ast", + "535": " ar", + "536": " î", + "537": "ze", + "538": " ra", + "539": "ho", + "540": "ju", + "541": "ör", + "542": "ки", + "543": "ль", + "544": " ni", + "545": "ви", + "546": "ло", + "547": " so", + "548": "ver", + "549": " pre", + "550": " я", + "551": " ta", + "552": " th", + "553": " е", + "554": "tu", + "555": " be", + "556": "да", + "557": "pp", + "558": "ind", + "559": "ge", + "560": " du", + "561": "ess", + "562": "ks", + "563": " od", + "564": "ku", + "565": "ся", + "566": "ac", + "567": "ele", + "568": "ια", + "569": "zy", + "570": "ran", + "571": "ten", + "572": "io", + "573": " în", + "574": "ма", + "575": " and", + "576": "де", + "577": " ka", + "578": " да", + "579": "ди", + "580": "ate", + "581": "iz", + "582": "ite", + "583": "sk", + "584": "ente", + "585": " у", + "586": " za", + "587": " per", + "588": " pri", + "589": " im", + "590": " som", + "591": "da", + "592": " of", + "593": "τα", + "594": " des", + "595": "τι", + "596": "ow", + "597": "ве", + "598": "ny", + "599": " op", + "600": "τε", + "601": " ob", + "602": " den", + "603": " ro", + "604": "ui", + "605": " до", + "606": "az", + "607": " про", + "608": "же", + "609": " ve", + "610": "ão", + "611": " ko", + "612": "iem", + "613": "не", + "614": " ver", + "615": " die", + "616": "ens", + "617": " mo", + "618": " as", + "619": "gy", + "620": "au", + "621": "ort", + "622": " δ", + "623": " der", + "624": " ș", + "625": " van", + "626": " min", + "627": " ch", + "628": "ier", + "629": "lu", + "630": "lo", + "631": " og", + "632": " att", + "633": " то", + "634": " č", + "635": "ji", + "636": "pr", + "637": " var", + "638": "du", + "639": " та", + "640": " tr", + "641": " ab", + "642": " om", + "643": " να", + "644": "що", + "645": "ust", + "646": "gen", + "647": " sk", + "648": "ми", + "649": "ál", + "650": "den", + "651": "ff", + "652": " by", + "653": "mi", + "654": "ás", + "655": "ati", + "656": " се", + "657": " und", + "658": " το", + "659": " um", + "660": "ля", + "661": " est", + "662": "га", + "663": "all", + "664": "ме", + "665": " iz", + "666": "eb", + "667": "ans", + "668": " τη", + "669": "op", + "670": "мо", + "671": " мо", + "672": " от", + "673": "ga", + "674": " mu", + "675": " а", + "676": "ys", + "677": " pas", + "678": " ка", + "679": " nu", + "680": " к", + "681": " Z", + "682": " del", + "683": " mi", + "684": "dz", + "685": "għ", + "686": " tal", + "687": " med", + "688": " tak", + "689": " och", + "690": "kt", + "691": " ac", + "692": " am", + "693": "ní", + "694": " au", + "695": "nd", + "696": "πο", + "697": "ont", + "698": " man", + "699": " pe", + "700": "iti", + "701": " και", + "702": " pod", + "703": "sz", + "704": " și", + "705": "ej", + "706": "ki", + "707": " pl", + "708": " ku", + "709": " av", + "710": " ви", + "711": "ord", + "712": "ha", + "713": " het", + "714": "ada", + "715": "το", + "716": "ρο", + "717": " pos", + "718": " т", + "719": "tä", + "720": "iet", + "721": "cht", + "722": "ale", + "723": "ру", + "724": "ina", + "725": "ck", + "726": "po", + "727": " že", + "728": "wa", + "729": "ρα", + "730": " tu", + "731": "ade", + "732": " nie", + "733": " έ", + "734": " sz", + "735": "ili", + "736": "ba", + "737": "át", + "738": " på", + "739": "pe", + "740": " ό", + "741": "по", + "742": " ра", + "743": " ex", + "744": "ija", + "745": "iss", + "746": " em", + "747": "ala", + "748": "ber", + "749": " ц", + "750": " we", + "751": "gi", + "752": " és", + "753": " ik", + "754": "μα", + "755": "ше", + "756": " os", + "757": " Т", + "758": " kom", + "759": "zi", + "760": " γ", + "761": "ation", + "762": "ond", + "763": "mer", + "764": "yt", + "765": " för", + "766": " sam", + "767": " ap", + "768": " ol", + "769": "ts", + "770": "be", + "771": "ні", + "772": " va", + "773": "ato", + "774": " che", + "775": "че", + "776": "чи", + "777": "za", + "778": " pu", + "779": "хо", + "780": " să", + "781": "ete", + "782": " een", + "783": " għ", + "784": "ät", + "785": "ven", + "786": "for", + "787": "ile", + "788": " az", + "789": "τη", + "790": "bi", + "791": "jo", + "792": " че", + "793": "né", + "794": "tor", + "795": "vi", + "796": "ca", + "797": " ri", + "798": "ți", + "799": " af", + "800": "ons", + "801": "ista", + "802": "äl", + "803": "му", + "804": "nie", + "805": " э", + "806": "до", + "807": "ικ", + "808": " por", + "809": " і", + "810": "mo", + "811": "ug", + "812": " Н", + "813": " he", + "814": "ogy", + "815": " para", + "816": "und", + "817": " das", + "818": " lo", + "819": " εί", + "820": " men", + "821": "és", + "822": " що", + "823": "pl", + "824": "με", + "825": "men", + "826": " ter", + "827": "ня", + "828": " В", + "829": " του", + "830": "ben", + "831": "ны", + "832": " à", + "833": " har", + "834": "се", + "835": " ha", + "836": "bu", + "837": " pol", + "838": " De", + "839": " or", + "840": " ο", + "841": "rav", + "842": " ein", + "843": " при", + "844": "di", + "845": "so", + "846": "за", + "847": "per", + "848": "art", + "849": " pa", + "850": " les", + "851": " vid", + "852": " cu", + "853": " kad", + "854": " vis", + "855": "oll", + "856": " ki", + "857": " är", + "858": "rat", + "859": "ча", + "860": " dat", + "861": "ett", + "862": " что", + "863": "hr", + "864": "che", + "865": " hogy", + "866": "бо", + "867": " co", + "868": "dy", + "869": " it", + "870": "ando", + "871": " En", + "872": " ce", + "873": " ge", + "874": "ung", + "875": " kan", + "876": "сто", + "877": "jen", + "878": " ser", + "879": "ще", + "880": " Ja", + "881": "ih", + "882": " го", + "883": "си", + "884": "ци", + "885": "ara", + "886": " sv", + "887": "ρι", + "888": " με", + "889": " ż", + "890": "ies", + "891": " Se", + "892": " In", + "893": "bl", + "894": "ado", + "895": " jo", + "896": "ult", + "897": "jon", + "898": "res", + "899": "že", + "900": " met", + "901": "der", + "902": "gu", + "903": " til", + "904": " П", + "905": " meg", + "906": " ü", + "907": "ér", + "908": " бу", + "909": " una", + "910": "ram", + "911": "ση", + "912": " ho", + "913": "ym", + "914": "ку", + "915": "ich", + "916": "ore", + "917": "εί", + "918": " из", + "919": "ід", + "920": "án", + "921": " tai", + "922": " С", + "923": "co", + "924": " was", + "925": "ila", + "926": "ement", + "927": "νο", + "928": " kon", + "929": " ist", + "930": "ím", + "931": "pa", + "932": "ela", + "933": " nem", + "934": " val", + "935": "ata", + "936": " tem", + "937": "ang", + "938": " tie", + "939": " pot", + "940": "esti", + "941": "ón", + "942": "int", + "943": "сти", + "944": " Д", + "945": "ста", + "946": "ento", + "947": "elle", + "948": "cz", + "949": " dar", + "950": " г", + "951": "ши", + "952": " пре", + "953": "bo", + "954": "ede", + "955": "red", + "956": " lu", + "957": "πό", + "958": "tr", + "959": "que", + "960": "ler", + "961": "ard", + "962": " Ma", + "963": "ego", + "964": " Al", + "965": "зи", + "966": "ну", + "967": "ini", + "968": "tav", + "969": " ei", + "970": " egy", + "971": " vol", + "972": "amo", + "973": "ib", + "974": "ste", + "975": " об", + "976": "ер", + "977": "ita", + "978": "ana", + "979": "ár", + "980": "жи", + "981": "kl", + "982": " во", + "983": " те", + "984": "ijn", + "985": "лю", + "986": "сь", + "987": "ég", + "988": "ht", + "989": "isk", + "990": " Y", + "991": "nen", + "992": "ega", + "993": " tra", + "994": "ám", + "995": "nos", + "996": "cu", + "997": "ako", + "998": "ave", + "999": " oli", + "1000": " К", + "1001": " kas", + "1002": "go", + "1003": " sem", + "1004": " To", + "1005": "ama", + "1006": "rit", + "1007": "ναι", + "1008": "ning", + "1009": " κα", + "1010": " М", + "1011": " că", + "1012": "ów", + "1013": "jem", + "1014": "ър", + "1015": "ít", + "1016": "kk", + "1017": " jed", + "1018": "ise", + "1019": " си", + "1020": " bi", + "1021": " res", + "1022": "ých", + "1023": " kun", + "1024": " ма", + "1025": " ale", + "1026": "era", + "1027": " mit", + "1028": "las", + "1029": "år", + "1030": " Ne", + "1031": " Je", + "1032": " się", + "1033": " ми", + "1034": "ly", + "1035": " θ", + "1036": " zu", + "1037": " ú", + "1038": " fil", + "1039": "ora", + "1040": "ния", + "1041": "att", + "1042": " bu", + "1043": "olo", + "1044": " dem", + "1045": "ika", + "1046": "ami", + "1047": "ito", + "1048": "eli", + "1049": "ano", + "1050": " that", + "1051": "zt", + "1052": " О", + "1053": "áv", + "1054": "ів", + "1055": "lig", + "1056": " για", + "1057": " př", + "1058": "ние", + "1059": "ду", + "1060": "elt", + "1061": "би", + "1062": " fin", + "1063": " viņ", + "1064": "ία", + "1065": "mu", + "1066": "eda", + "1067": " sig", + "1068": " ħ", + "1069": "υτ", + "1070": "ων", + "1071": "uro", + "1072": "ūs", + "1073": "eren", + "1074": " де", + "1075": "ct", + "1076": " ich", + "1077": "λλ", + "1078": "len", + "1079": "αν", + "1080": "ari", + "1081": " iš", + "1082": "sp", + "1083": "ces", + "1084": " vy", + "1085": "ida", + "1086": "лу", + "1087": " hu", + "1088": " į", + "1089": "ās", + "1090": " uz", + "1091": " inter", + "1092": " ро", + "1093": "agħ", + "1094": "ky", + "1095": "εν", + "1096": "ono", + "1097": "cia", + "1098": "ry", + "1099": "ori", + "1100": " Il", + "1101": " ent", + "1102": " pra", + "1103": " my", + "1104": "ille", + "1105": " Es", + "1106": "ft", + "1107": " dis", + "1108": " pers", + "1109": " είναι", + "1110": " ad", + "1111": " all", + "1112": "ani", + "1113": "unt", + "1114": " Un", + "1115": "τικ", + "1116": "one", + "1117": " tre", + "1118": " fr", + "1119": "ante", + "1120": " И", + "1121": "жа", + "1122": "sen", + "1123": " ότι", + "1124": "oj", + "1125": " это", + "1126": "ві", + "1127": " ke", + "1128": "ār", + "1129": "ane", + "1130": "па", + "1131": "do", + "1132": "ame", + "1133": "äm", + "1134": "ici", + "1135": "ment", + "1136": " cont", + "1137": "со", + "1138": " που", + "1139": "ban", + "1140": "ica", + "1141": " mis", + "1142": "ет", + "1143": "alt", + "1144": "ül", + "1145": "én", + "1146": " è", + "1147": "vat", + "1148": " ty", + "1149": " gr", + "1150": " din", + "1151": "ön", + "1152": " са", + "1153": " από", + "1154": " los", + "1155": " mod", + "1156": "ene", + "1157": " бы", + "1158": "itu", + "1159": " но", + "1160": "led", + "1161": " Det", + "1162": "ού", + "1163": "use", + "1164": " bet", + "1165": " este", + "1166": "ря", + "1167": " зна", + "1168": " την", + "1169": " St", + "1170": " ut", + "1171": "enn", + "1172": " pi", + "1173": " ze", + "1174": "uk", + "1175": "este", + "1176": "zn", + "1177": " mar", + "1178": "ного", + "1179": " ре", + "1180": " you", + "1181": " till", + "1182": " Q", + "1183": "ione", + "1184": "či", + "1185": " han", + "1186": "inu", + "1187": " tas", + "1188": "pu", + "1189": "ink", + "1190": "itt", + "1191": " ak", + "1192": " dan", + "1193": " раз", + "1194": " pour", + "1195": " це", + "1196": " hab", + "1197": " tod", + "1198": "äh", + "1199": "chen", + "1200": "bli", + "1201": "τά", + "1202": "tiv", + "1203": " wir", + "1204": " imp", + "1205": "ice", + "1206": "zz", + "1207": "пи", + "1208": "eri", + "1209": " sch", + "1210": " ti", + "1211": " ju", + "1212": " ot", + "1213": " tik", + "1214": "isi", + "1215": " со", + "1216": "iek", + "1217": "entru", + "1218": " jag", + "1219": "yn", + "1220": " dob", + "1221": "enti", + "1222": "рі", + "1223": " så", + "1224": "ima", + "1225": " χ", + "1226": "ert", + "1227": " raz", + "1228": "ied", + "1229": "wo", + "1230": "ime", + "1231": "ino", + "1232": " pat", + "1233": " że", + "1234": "ρά", + "1235": "ту", + "1236": "ose", + "1237": " kur", + "1238": " pal", + "1239": "her", + "1240": "tion", + "1241": " võ", + "1242": " bil", + "1243": " ва", + "1244": "ουν", + "1245": " No", + "1246": " nek", + "1247": "še", + "1248": "we", + "1249": " inte", + "1250": " ved", + "1251": " sav", + "1252": "ger", + "1253": " Я", + "1254": " see", + "1255": "ken", + "1256": "ši", + "1257": " Ch", + "1258": "ema", + "1259": " А", + "1260": "άν", + "1261": " jeg", + "1262": "pi", + "1263": " fa", + "1264": " mai", + "1265": "eni", + "1266": " ga", + "1267": "ide", + "1268": "сть", + "1269": " ste", + "1270": "пе", + "1271": " συ", + "1272": "les", + "1273": "ux", + "1274": " sta", + "1275": "ін", + "1276": " nad", + "1277": "ės", + "1278": " як", + "1279": "val", + "1280": "ém", + "1281": "emm", + "1282": " uh", + "1283": " br", + "1284": " js", + "1285": " zijn", + "1286": "inen", + "1287": " las", + "1288": "var", + "1289": " care", + "1290": "nu", + "1291": "usi", + "1292": "amente", + "1293": "pre", + "1294": " επ", + "1295": " вы", + "1296": "ää", + "1297": "unk", + "1298": "ike", + "1299": "het", + "1300": " ten", + "1301": " sie", + "1302": "ться", + "1303": "ía", + "1304": "ür", + "1305": " An", + "1306": " dom", + "1307": "tig", + "1308": "ser", + "1309": " La", + "1310": "tre", + "1311": "amos", + "1312": " τα", + "1313": " tro", + "1314": " non", + "1315": " q", + "1316": " fi", + "1317": "man", + "1318": "ги", + "1319": " pentru", + "1320": "ισ", + "1321": " bo", + "1322": " съ", + "1323": "ρε", + "1324": " η", + "1325": "up", + "1326": " kol", + "1327": "ži", + "1328": "ām", + "1329": " aby", + "1330": " gen", + "1331": " tam", + "1332": "ova", + "1333": " Le", + "1334": "ese", + "1335": " Α", + "1336": " wh", + "1337": "ove", + "1338": "ολ", + "1339": " Sie", + "1340": " β", + "1341": "лі", + "1342": " ф", + "1343": "imo", + "1344": "vis", + "1345": " une", + "1346": "ρό", + "1347": " wa", + "1348": "ně", + "1349": "ott", + "1350": " jak", + "1351": " бе", + "1352": " fu", + "1353": "eta", + "1354": "van", + "1355": "ét", + "1356": "uma", + "1357": " nä", + "1358": "uj", + "1359": " Vi", + "1360": "esta", + "1361": " fo", + "1362": " ikke", + "1363": " kien", + "1364": "mente", + "1365": " може", + "1366": " ни", + "1367": "ums", + "1368": " että", + "1369": " hän", + "1370": "ije", + "1371": " nos", + "1372": "ige", + "1373": "āk", + "1374": " как", + "1375": " vien", + "1376": "sta", + "1377": " mil", + "1378": "ero", + "1379": "ці", + "1380": " reg", + "1381": "lj", + "1382": " Qu", + "1383": "ого", + "1384": "oli", + "1385": " jest", + "1386": "cie", + "1387": "esto", + "1388": " over", + "1389": "af", + "1390": " хо", + "1391": " φ", + "1392": " αυτ", + "1393": " στο", + "1394": "joni", + "1395": " dru", + "1396": "ται", + "1397": " aut", + "1398": "σε", + "1399": " voor", + "1400": " mal", + "1401": "pt", + "1402": "ras", + "1403": "ån", + "1404": "eme", + "1405": "ła", + "1406": "cion", + "1407": " Na", + "1408": " vous", + "1409": " wer", + "1410": "urs", + "1411": "ør", + "1412": "ры", + "1413": "ick", + "1414": "cê", + "1415": " nas", + "1416": " ö", + "1417": "ção", + "1418": " kr", + "1419": "ena", + "1420": "ха", + "1421": " uma", + "1422": " vai", + "1423": "hom", + "1424": " ind", + "1425": " все", + "1426": " zn", + "1427": " como", + "1428": "ins", + "1429": "ende", + "1430": " mor", + "1431": "ере", + "1432": " това", + "1433": " als", + "1434": "ταν", + "1435": "ely", + "1436": " svo", + "1437": " ud", + "1438": "tó", + "1439": " vel", + "1440": "пра", + "1441": " us", + "1442": "zen", + "1443": "hn", + "1444": "ción", + "1445": " Б", + "1446": " vil", + "1447": "ps", + "1448": " naj", + "1449": "une", + "1450": " yra", + "1451": " x", + "1452": " roz", + "1453": "мен", + "1454": " ktor", + "1455": " go", + "1456": "wi", + "1457": "aks", + "1458": "ella", + "1459": " probl", + "1460": "ity", + "1461": " θα", + "1462": " bar", + "1463": "са", + "1464": "σει", + "1465": " mais", + "1466": " αν", + "1467": " То", + "1468": " sl", + "1469": " comp", + "1470": " não", + "1471": "wn", + "1472": "ive", + "1473": " von", + "1474": " qui", + "1475": "vel", + "1476": "lich", + "1477": "nosti", + "1478": "min", + "1479": " war", + "1480": "uje", + "1481": " niet", + "1482": " ή", + "1483": "ιο", + "1484": "iau", + "1485": "ään", + "1486": "ým", + "1487": "μέ", + "1488": "ament", + "1489": "ole", + "1490": " sin", + "1491": " not", + "1492": " Ar", + "1493": " pom", + "1494": " vor", + "1495": "āt", + "1496": "por", + "1497": " bud", + "1498": " wy", + "1499": "ба", + "1500": "isti", + "1501": " aj", + "1502": " The", + "1503": " tur", + "1504": " sur", + "1505": "ят", + "1506": "jet", + "1507": "gan", + "1508": " Ε", + "1509": " kor", + "1510": "iex", + "1511": " nicht", + "1512": " dal", + "1513": "ρί", + "1514": "eld", + "1515": "іль", + "1516": "let", + "1517": " kar", + "1518": " dr", + "1519": "uri", + "1520": "by", + "1521": "iden", + "1522": "ern", + "1523": " ako", + "1524": " σε", + "1525": "că", + "1526": "bil", + "1527": " dans", + "1528": "utta", + "1529": "asta", + "1530": " sm", + "1531": "ret", + "1532": " λ", + "1533": "ido", + "1534": "age", + "1535": " her", + "1536": " προ", + "1537": " mas", + "1538": "inn", + "1539": " бо", + "1540": " dass", + "1541": " пра", + "1542": "kom", + "1543": " З", + "1544": " mat", + "1545": " kā", + "1546": " El", + "1547": " від", + "1548": "ší", + "1549": " wie", + "1550": "pro", + "1551": " tar", + "1552": " tud", + "1553": " ca", + "1554": "kon", + "1555": " πε", + "1556": " па", + "1557": "lim", + "1558": " bes", + "1559": " kai", + "1560": "ros", + "1561": " для", + "1562": " pred", + "1563": "ву", + "1564": "νε", + "1565": " era", + "1566": " ave", + "1567": " bū", + "1568": "nik", + "1569": "ith", + "1570": " któ", + "1571": "ors", + "1572": "щи", + "1573": " ста", + "1574": " tom", + "1575": " mus", + "1576": " gal", + "1577": "cer", + "1578": "tt", + "1579": " Š", + "1580": " leg", + "1581": "rij", + "1582": " Τ", + "1583": " Euro", + "1584": " Tai", + "1585": "form", + "1586": " ст", + "1587": " ме", + "1588": " div", + "1589": "ον", + "1590": " ду", + "1591": "ска", + "1592": " fl", + "1593": " auf", + "1594": " cons", + "1595": " ci", + "1596": " так", + "1597": "ket", + "1598": " kö", + "1599": " ali", + "1600": " dos", + "1601": "ουμε", + "1602": "vad", + "1603": " str", + "1604": "itä", + "1605": "ase", + "1606": " nog", + "1607": "ann", + "1608": "лен", + "1609": "fic", + "1610": " υ", + "1611": "fer", + "1612": " Κ", + "1613": " сво", + "1614": "elo", + "1615": "ec", + "1616": "pos", + "1617": " sen", + "1618": " nous", + "1619": " Com", + "1620": " ли", + "1621": "зо", + "1622": " sim", + "1623": " más", + "1624": " δεν", + "1625": " Po", + "1626": "īb", + "1627": " gl", + "1628": " prog", + "1629": "ekk", + "1630": "ure", + "1631": " πα", + "1632": "ски", + "1633": " sc", + "1634": " tri", + "1635": " sob", + "1636": " kui", + "1637": " ag", + "1638": "kun", + "1639": " bl", + "1640": " ще", + "1641": "ών", + "1642": " hm", + "1643": "ρέ", + "1644": "cy", + "1645": "tim", + "1646": " ji", + "1647": " have", + "1648": "alo", + "1649": " ole", + "1650": "ól", + "1651": "vet", + "1652": "rt", + "1653": " Tak", + "1654": " person", + "1655": " ча", + "1656": " tagħ", + "1657": "asi", + "1658": " στη", + "1659": "šan", + "1660": "zd", + "1661": " fel", + "1662": " inc", + "1663": "EE", + "1664": "λο", + "1665": " ένα", + "1666": " ве", + "1667": " você", + "1668": " mig", + "1669": " už", + "1670": "ща", + "1671": " Mar", + "1672": "ág", + "1673": " heb", + "1674": "ēs", + "1675": " vä", + "1676": " Da", + "1677": " bol", + "1678": " ih", + "1679": "ści", + "1680": "vor", + "1681": "št", + "1682": "λα", + "1683": " sus", + "1684": "ker", + "1685": " cs", + "1686": " kter", + "1687": "uld", + "1688": "eks", + "1689": " nes", + "1690": "сно", + "1691": "bb", + "1692": "ava", + "1693": " alle", + "1694": " Par", + "1695": "war", + "1696": "ēj", + "1697": "iko", + "1698": "rad", + "1699": "nost", + "1700": " pla", + "1701": "yl", + "1702": "par", + "1703": "ovan", + "1704": "ân", + "1705": " dé", + "1706": " vers", + "1707": "nak", + "1708": " vide", + "1709": " eu", + "1710": "ette", + "1711": " cer", + "1712": "lik", + "1713": "мі", + "1714": " are", + "1715": "oni", + "1716": "ák", + "1717": " ou", + "1718": " pov", + "1719": "hed", + "1720": " ah", + "1721": "gel", + "1722": "hen", + "1723": "ło", + "1724": "ак", + "1725": "hu", + "1726": "ний", + "1727": "ün", + "1728": "ss", + "1729": " don", + "1730": "це", + "1731": " hat", + "1732": "κε", + "1733": " х", + "1734": " nov", + "1735": "hat", + "1736": "imi", + "1737": " mind", + "1738": " tiegħ", + "1739": " buvo", + "1740": " inv", + "1741": "oc", + "1742": "ner", + "1743": " car", + "1744": "λε", + "1745": "él", + "1746": " produ", + "1747": "kol", + "1748": "ados", + "1749": " tä", + "1750": "fter", + "1751": " gro", + "1752": " Be", + "1753": "nej", + "1754": "yst", + "1755": "almente", + "1756": " Σ", + "1757": " plus", + "1758": " Pro", + "1759": "жно", + "1760": "áz", + "1761": "ekt", + "1762": "бе", + "1763": " mer", + "1764": "nes", + "1765": " pie", + "1766": " τι", + "1767": "asti", + "1768": "ните", + "1769": " Te", + "1770": "agy", + "1771": " ć", + "1772": " mog", + "1773": " ба", + "1774": "ulle", + "1775": "its", + "1776": "ativ", + "1777": "oti", + "1778": "my", + "1779": " τον", + "1780": "ats", + "1781": "льно", + "1782": " По", + "1783": "ona", + "1784": " pres", + "1785": "kel", + "1786": " кото", + "1787": " Ale", + "1788": " aan", + "1789": " пере", + "1790": "rem", + "1791": " wi", + "1792": "ingap", + "1793": "су", + "1794": "āj", + "1795": "ità", + "1796": " id", + "1797": "yv", + "1798": " Е", + "1799": "ingen", + "1800": " мен", + "1801": "лед", + "1802": "ater", + "1803": "ue", + "1804": " tran", + "1805": " Me", + "1806": "σι", + "1807": " oma", + "1808": "ilo", + "1809": " Π", + "1810": " sol", + "1811": " dig", + "1812": "ство", + "1813": " Jo", + "1814": "ор", + "1815": "ah", + "1816": " eller", + "1817": " Sch", + "1818": "ша", + "1819": "oč", + "1820": " μα", + "1821": " под", + "1822": "oz", + "1823": "λη", + "1824": " prin", + "1825": " kl", + "1826": "τή", + "1827": " лю", + "1828": "ско", + "1829": " biex", + "1830": " eine", + "1831": " ré", + "1832": "чно", + "1833": " dz", + "1834": "emos", + "1835": "akt", + "1836": "ær", + "1837": " dire", + "1838": "ass", + "1839": " għal", + "1840": " up", + "1841": "emb", + "1842": " We", + "1843": " dol", + "1844": "jer", + "1845": " έχ", + "1846": "μο", + "1847": "ura", + "1848": " bija", + "1849": " mon", + "1850": " lai", + "1851": " На", + "1852": "esso", + "1853": " Er", + "1854": "cij", + "1855": "iste", + "1856": " én", + "1857": " nap", + "1858": "šk", + "1859": "гу", + "1860": "ysl", + "1861": "сте", + "1862": "ého", + "1863": "ěl", + "1864": " kap", + "1865": "anti", + "1866": " cor", + "1867": "kr", + "1868": " with", + "1869": " Μ", + "1870": "ong", + "1871": " cre", + "1872": "зу", + "1873": "ande", + "1874": "ív", + "1875": " rec", + "1876": " He", + "1877": "ργ", + "1878": " star", + "1879": "νη", + "1880": " zd", + "1881": " tā", + "1882": "czy", + "1883": "hod", + "1884": " ny", + "1885": "cs", + "1886": " cz", + "1887": "ald", + "1888": " У", + "1889": " što", + "1890": "ný", + "1891": "ēr", + "1892": "pli", + "1893": " poz", + "1894": " pop", + "1895": " int", + "1896": "ach", + "1897": "ijiet", + "1898": "anto", + "1899": " Ich", + "1900": " sich", + "1901": "ome", + "1902": "enta", + "1903": " come", + "1904": " kä", + "1905": " sab", + "1906": " bli", + "1907": " app", + "1908": "ád", + "1909": " pon", + "1910": "ем", + "1911": "elen", + "1912": "vol", + "1913": " przy", + "1914": " naš", + "1915": "ais", + "1916": "ând", + "1917": " cam", + "1918": "ння", + "1919": " lah", + "1920": " Ik", + "1921": "åg", + "1922": " Oh", + "1923": " had", + "1924": " mag", + "1925": "rie", + "1926": " κά", + "1927": "λλά", + "1928": " nav", + "1929": "ły", + "1930": "ρω", + "1931": "ρη", + "1932": " mill", + "1933": "pri", + "1934": " kont", + "1935": "ří", + "1936": " nor", + "1937": " Men", + "1938": " esta", + "1939": " zo", + "1940": " maj", + "1941": "вер", + "1942": " ru", + "1943": "des", + "1944": " zap", + "1945": " sag", + "1946": " pou", + "1947": " дру", + "1948": " Ta", + "1949": " би", + "1950": " Ο", + "1951": "fi", + "1952": " für", + "1953": " vý", + "1954": "ėl", + "1955": " aus", + "1956": "yk", + "1957": " Jag", + "1958": "fe", + "1959": " ά", + "1960": " So", + "1961": " kell", + "1962": "eti", + "1963": "ull", + "1964": "зна", + "1965": " dit", + "1966": " пред", + "1967": "anie", + "1968": "κα", + "1969": " gra", + "1970": "ties", + "1971": " sel", + "1972": "ams", + "1973": "mis", + "1974": " this", + "1975": "endo", + "1976": " And", + "1977": " pak", + "1978": " zá", + "1979": "hi", + "1980": " gu", + "1981": " ur", + "1982": "hd", + "1983": " ger", + "1984": " nat", + "1985": " tot", + "1986": "ení", + "1987": " μια", + "1988": "olog", + "1989": " й", + "1990": " Re", + "1991": " hal", + "1992": "zu", + "1993": "ijo", + "1994": " vir", + "1995": " pad", + "1996": " αυτό", + "1997": "una", + "1998": "su", + "1999": "net", + "2000": "пу", + "2001": " fra", + "2002": "mes", + "2003": "land", + "2004": " мы", + "2005": " Vo", + "2006": "ді", + "2007": "osa", + "2008": " É", + "2009": " out", + "2010": "osta", + "2011": " Nu", + "2012": "kö", + "2013": "ades", + "2014": "iten", + "2015": "oma", + "2016": " ie", + "2017": " kõ", + "2018": "cis", + "2019": " let", + "2020": " huwa", + "2021": " kam", + "2022": " vr", + "2023": " cas", + "2024": " sal", + "2025": " god", + "2026": "ová", + "2027": " Ко", + "2028": "anje", + "2029": "wie", + "2030": "esz", + "2031": "lä", + "2032": " está", + "2033": " sono", + "2034": " mes", + "2035": "вати", + "2036": "ului", + "2037": "mas", + "2038": "yd", + "2039": "ъл", + "2040": " ker", + "2041": "ev", + "2042": " има", + "2043": " fost", + "2044": "als", + "2045": " или", + "2046": "гра", + "2047": " cel", + "2048": " просто", + "2049": " Р", + "2050": " Pr", + "2051": " mom", + "2052": "ēl", + "2053": " kuri", + "2054": "xx", + "2055": " tö", + "2056": "oka", + "2057": " bez", + "2058": "жу", + "2059": " can", + "2060": " ide", + "2061": " má", + "2062": "ná", + "2063": "ii", + "2064": "ça", + "2065": "iku", + "2066": "лко", + "2067": " Ha", + "2068": "ната", + "2069": " ш", + "2070": " dok", + "2071": " Singap", + "2072": "сі", + "2073": "στε", + "2074": "ych", + "2075": "ény", + "2076": "gs", + "2077": "ēt", + "2078": " ő", + "2079": "nih", + "2080": " δια", + "2081": "oro", + "2082": " Het", + "2083": " Jeg", + "2084": "mit", + "2085": "ew", + "2086": "bar", + "2087": "udz", + "2088": " wor", + "2089": "ът", + "2090": " oc", + "2091": "ći", + "2092": " lab", + "2093": "ico", + "2094": " жи", + "2095": "ores", + "2096": " ez", + "2097": " ov", + "2098": " ї", + "2099": "ble", + "2100": "jud", + "2101": "elj", + "2102": " ai", + "2103": "cent", + "2104": " neg", + "2105": " parti", + "2106": " Die", + "2107": "rol", + "2108": " minn", + "2109": " μπο", + "2110": " tut", + "2111": "ρχ", + "2112": " cap", + "2113": "гда", + "2114": " volt", + "2115": " Но", + "2116": " prof", + "2117": " seda", + "2118": "sch", + "2119": " pen", + "2120": "бы", + "2121": " gy", + "2122": "ăr", + "2123": "дно", + "2124": "rov", + "2125": "ania", + "2126": " beg", + "2127": "umu", + "2128": " col", + "2129": " Г", + "2130": "rig", + "2131": "isen", + "2132": " kat", + "2133": "lev", + "2134": " ett", + "2135": " ně", + "2136": " nep", + "2137": "ék", + "2138": " parte", + "2139": "pen", + "2140": " ġ", + "2141": " polit", + "2142": "iej", + "2143": "ād", + "2144": "ty", + "2145": "ρώ", + "2146": "br", + "2147": " vš", + "2148": " ok", + "2149": "бу", + "2150": " son", + "2151": "σα", + "2152": "ată", + "2153": "θε", + "2154": "ajo", + "2155": " ξ", + "2156": " sú", + "2157": "ιά", + "2158": " væ", + "2159": " väl", + "2160": " kot", + "2161": " alla", + "2162": " jako", + "2163": "lad", + "2164": " fac", + "2165": "ħal", + "2166": " fuq", + "2167": " nar", + "2168": " še", + "2169": " мі", + "2170": " jsem", + "2171": " živ", + "2172": "issa", + "2173": " μέ", + "2174": " prze", + "2175": "del", + "2176": " Is", + "2177": " ele", + "2178": " dur", + "2179": " като", + "2180": "szy", + "2181": " Ir", + "2182": " inst", + "2183": "жда", + "2184": "чен", + "2185": " ned", + "2186": "afna", + "2187": "ola", + "2188": "īt", + "2189": "ère", + "2190": "igen", + "2191": " ná", + "2192": "ank", + "2193": " import", + "2194": " ну", + "2195": "gl", + "2196": "πε", + "2197": " є", + "2198": " mõ", + "2199": "osti", + "2200": "об", + "2201": " των", + "2202": "ете", + "2203": "ovor", + "2204": "uv", + "2205": "els", + "2206": "eto", + "2207": " rel", + "2208": "ют", + "2209": "emo", + "2210": " Th", + "2211": " οι", + "2212": "eno", + "2213": " teg", + "2214": "uni", + "2215": " tako", + "2216": " cent", + "2217": "ité", + "2218": "cul", + "2219": " ont", + "2220": " tag", + "2221": " Et", + "2222": " kes", + "2223": "cho", + "2224": "tak", + "2225": "isu", + "2226": "nek", + "2227": " ин", + "2228": "ument", + "2229": "гато", + "2230": "каза", + "2231": " sind", + "2232": "дин", + "2233": " Så", + "2234": "τό", + "2235": " inf", + "2236": "ца", + "2237": "áll", + "2238": "тел", + "2239": " Č", + "2240": " Han", + "2241": "про", + "2242": "taa", + "2243": " smo", + "2244": " lui", + "2245": "ute", + "2246": " diff", + "2247": " part", + "2248": "bre", + "2249": "oks", + "2250": "ēc", + "2251": " ig", + "2252": " qed", + "2253": " кон", + "2254": "τέ", + "2255": " pār", + "2256": " spe", + "2257": "ті", + "2258": "ġġ", + "2259": "eten", + "2260": " много", + "2261": " tun", + "2262": " tv", + "2263": " alg", + "2264": "ée", + "2265": "ance", + "2266": "nych", + "2267": "aju", + "2268": " mol", + "2269": " sobre", + "2270": "lus", + "2271": " pok", + "2272": " tim", + "2273": "ány", + "2274": "ally", + "2275": "isten", + "2276": " må", + "2277": " Ц", + "2278": " kommer", + "2279": " μου", + "2280": "kov", + "2281": " has", + "2282": " ¿", + "2283": "onom", + "2284": " uit", + "2285": "αα", + "2286": "ns", + "2287": " πολ", + "2288": "iec", + "2289": " dv", + "2290": " jūs", + "2291": " vagy", + "2292": "ść", + "2293": " Э", + "2294": "old", + "2295": " въ", + "2296": "cial", + "2297": " роз", + "2298": " ти", + "2299": "ire", + "2300": "ip", + "2301": "erg", + "2302": "iks", + "2303": " vl", + "2304": " mūs", + "2305": " bij", + "2306": " εν", + "2307": " dies", + "2308": " isso", + "2309": "ijk", + "2310": " mul", + "2311": " στην", + "2312": " Ez", + "2313": " lahko", + "2314": " mož", + "2315": " Du", + "2316": "hl", + "2317": "ět", + "2318": " Не", + "2319": " Ș", + "2320": "gt", + "2321": " ví", + "2322": "zer", + "2323": " ska", + "2324": " dra", + "2325": " seg", + "2326": " Х", + "2327": "our", + "2328": " geb", + "2329": " sunt", + "2330": " nam", + "2331": "owa", + "2332": " tek", + "2333": "ved", + "2334": "ства", + "2335": "ões", + "2336": " It", + "2337": " gran", + "2338": " program", + "2339": " ant", + "2340": "aire", + "2341": "ност", + "2342": " Si", + "2343": " See", + "2344": "σω", + "2345": "vu", + "2346": " end", + "2347": " hij", + "2348": " Έ", + "2349": "lij", + "2350": "iał", + "2351": " Î", + "2352": "iment", + "2353": " ħafna", + "2354": "rod", + "2355": " ass", + "2356": "aliz", + "2357": "mel", + "2358": "ята", + "2359": " fol", + "2360": "sti", + "2361": "vid", + "2362": " haben", + "2363": " Mo", + "2364": " della", + "2365": " più", + "2366": "forma", + "2367": "ös", + "2368": " Man", + "2369": "ude", + "2370": "ligt", + "2371": "ето", + "2372": " απο", + "2373": "μά", + "2374": "met", + "2375": "emp", + "2376": "assa", + "2377": "cí", + "2378": " нас", + "2379": "жен", + "2380": " upp", + "2381": " kuid", + "2382": "inā", + "2383": " Η", + "2384": " poč", + "2385": "pad", + "2386": "pres", + "2387": "lar", + "2388": " fam", + "2389": "сли", + "2390": "yb", + "2391": "aka", + "2392": "mar", + "2393": " taip", + "2394": "ран", + "2395": "πα", + "2396": "τί", + "2397": "них", + "2398": "ets", + "2399": "ings", + "2400": "avo", + "2401": "οι", + "2402": " finan", + "2403": "yti", + "2404": "ition", + "2405": " As", + "2406": "uks", + "2407": " περι", + "2408": "ämä", + "2409": " alt", + "2410": " ris", + "2411": "té", + "2412": "ira", + "2413": " Und", + "2414": "ton", + "2415": " lä", + "2416": "ren", + "2417": "ider", + "2418": "вы", + "2419": " Og", + "2420": " gan", + "2421": "aten", + "2422": "čno", + "2423": " efter", + "2424": " спо", + "2425": " fe", + "2426": " dev", + "2427": "now", + "2428": "entes", + "2429": "ные", + "2430": " vez", + "2431": "usta", + "2432": "uto", + "2433": " rad", + "2434": " pun", + "2435": "koll", + "2436": " Est", + "2437": " dost", + "2438": "спо", + "2439": " akt", + "2440": " oh", + "2441": " вре", + "2442": " under", + "2443": "зва", + "2444": " Л", + "2445": " Im", + "2446": " sku", + "2447": "кий", + "2448": " sil", + "2449": " которы", + "2450": " sua", + "2451": "ars", + "2452": " Да", + "2453": " ost", + "2454": "ээ", + "2455": "elu", + "2456": "ria", + "2457": " hel", + "2458": " kos", + "2459": " kaip", + "2460": " għand", + "2461": "ioni", + "2462": "jih", + "2463": " ня", + "2464": " net", + "2465": " pap", + "2466": " prost", + "2467": "ты", + "2468": "ють", + "2469": " mēs", + "2470": " här", + "2471": "tek", + "2472": " Am", + "2473": "igt", + "2474": "vē", + "2475": "üü", + "2476": " jos", + "2477": "tait", + "2478": " І", + "2479": " hav", + "2480": "шо", + "2481": "reb", + "2482": " pag", + "2483": "ходи", + "2484": "rid", + "2485": " naar", + "2486": " prav", + "2487": "orn", + "2488": " mot", + "2489": "ίν", + "2490": "cha", + "2491": "ral", + "2492": " continu", + "2493": "ld", + "2494": " far", + "2495": " stud", + "2496": " ess", + "2497": " Ver", + "2498": " он", + "2499": "ības", + "2500": "ėj", + "2501": "ėjo", + "2502": "kin", + "2503": " kra", + "2504": " avec", + "2505": "εια", + "2506": "raz", + "2507": " problem", + "2508": " ήταν", + "2509": "σο", + "2510": "pet", + "2511": "ръ", + "2512": "γμα", + "2513": " organ", + "2514": " vu", + "2515": "ils", + "2516": "чу", + "2517": "azz", + "2518": "дна", + "2519": " pä", + "2520": " len", + "2521": " рабо", + "2522": "če", + "2523": "ätt", + "2524": "ρεί", + "2525": " werden", + "2526": "ume", + "2527": "ито", + "2528": "arb", + "2529": " jis", + "2530": " they", + "2531": "ín", + "2532": " dec", + "2533": "вори", + "2534": " сто", + "2535": " daug", + "2536": " plan", + "2537": " savo", + "2538": "illa", + "2539": " maar", + "2540": "ès", + "2541": " vad", + "2542": "ilor", + "2543": "ub", + "2544": " qual", + "2545": "gal", + "2546": "hal", + "2547": "вно", + "2548": "кти", + "2549": " су", + "2550": " ди", + "2551": "ack", + "2552": " koji", + "2553": "ική", + "2554": "вен", + "2555": " wo", + "2556": " also", + "2557": " någ", + "2558": "lan", + "2559": " За", + "2560": " кра", + "2561": " są", + "2562": " Ч", + "2563": " Den", + "2564": " sme", + "2565": " bed", + "2566": " він", + "2567": " zv", + "2568": "вя", + "2569": "get", + "2570": " Viņ", + "2571": "osi", + "2572": "cip", + "2573": " під", + "2574": " stran", + "2575": " hun", + "2576": " tid", + "2577": " Bet", + "2578": "jeg", + "2579": " õ", + "2580": "cess", + "2581": "raw", + "2582": "ден", + "2583": " които", + "2584": "ных", + "2585": " Kom", + "2586": "mon", + "2587": "oda", + "2588": " Och", + "2589": "irm", + "2590": "jn", + "2591": "ép", + "2592": "area", + "2593": "θη", + "2594": " bien", + "2595": " huma", + "2596": "ovi", + "2597": " kok", + "2598": "essa", + "2599": "ša", + "2600": " ši", + "2601": " hebben", + "2602": " ba", + "2603": "bol", + "2604": " mak", + "2605": " ști", + "2606": "ska", + "2607": "ész", + "2608": "čin", + "2609": " така", + "2610": "hm", + "2611": " prz", + "2612": " sein", + "2613": " siis", + "2614": "vil", + "2615": "ją", + "2616": "λά", + "2617": " put", + "2618": "ua", + "2619": " Ν", + "2620": "ünk", + "2621": " situ", + "2622": "ей", + "2623": "пре", + "2624": " ін", + "2625": " sell", + "2626": "alla", + "2627": "yslím", + "2628": " sau", + "2629": " ле", + "2630": "wer", + "2631": "isto", + "2632": "pod", + "2633": " enc", + "2634": " New", + "2635": " čas", + "2636": "rar", + "2637": "if", + "2638": "rá", + "2639": " bilo", + "2640": "šu", + "2641": " ven", + "2642": "ee", + "2643": "ρού", + "2644": " буде", + "2645": "тя", + "2646": "leg", + "2647": "esi", + "2648": " му", + "2649": " num", + "2650": "πά", + "2651": " pens", + "2652": " hija", + "2653": " samo", + "2654": " selle", + "2655": " sec", + "2656": " like", + "2657": " bom", + "2658": "elf", + "2659": "вля", + "2660": "ért", + "2661": " veľ", + "2662": "tų", + "2663": "vit", + "2664": " Δ", + "2665": "pon", + "2666": "ță", + "2667": "ük", + "2668": " беше", + "2669": "ât", + "2670": "fin", + "2671": " szer", + "2672": " πο", + "2673": "any", + "2674": " Nie", + "2675": "iga", + "2676": " uns", + "2677": " gru", + "2678": " one", + "2679": " пос", + "2680": "χε", + "2681": " but", + "2682": "jed", + "2683": "isa", + "2684": " jot", + "2685": "йте", + "2686": " kön", + "2687": " quando", + "2688": "oi", + "2689": " kal", + "2690": " wat", + "2691": " nost", + "2692": " post", + "2693": " Um", + "2694": " sul", + "2695": " nagy", + "2696": " xi", + "2697": " waar", + "2698": "los", + "2699": "sten", + "2700": "esse", + "2701": " tap", + "2702": " sak", + "2703": "anja", + "2704": " Ze", + "2705": "amento", + "2706": "πει", + "2707": "ają", + "2708": " gre", + "2709": " questo", + "2710": "și", + "2711": " Ali", + "2712": " ку", + "2713": " vet", + "2714": "uden", + "2715": " sn", + "2716": "idu", + "2717": " nej", + "2718": "dzie", + "2719": "ís", + "2720": "ivers", + "2721": " pog", + "2722": " čo", + "2723": "izz", + "2724": "tai", + "2725": " εκ", + "2726": " ljud", + "2727": " će", + "2728": "τικό", + "2729": " form", + "2730": " taj", + "2731": "cio", + "2732": "indi", + "2733": "ll", + "2734": " sub", + "2735": " чтобы", + "2736": "vez", + "2737": " gar", + "2738": " най", + "2739": "ish", + "2740": " nik", + "2741": "istä", + "2742": "erd", + "2743": " vz", + "2744": "ez", + "2745": " his", + "2746": "нь", + "2747": "oval", + "2748": "λή", + "2749": " επι", + "2750": "inti", + "2751": "ій", + "2752": " arī", + "2753": " sont", + "2754": " щоб", + "2755": " prie", + "2756": " liv", + "2757": " tõ", + "2758": " moż", + "2759": " стра", + "2760": "avad", + "2761": " ben", + "2762": "ident", + "2763": " lid", + "2764": "īdz", + "2765": " чи", + "2766": "ales", + "2767": "iter", + "2768": "kal", + "2769": "odu", + "2770": " Și", + "2771": " pass", + "2772": "ване", + "2773": "ony", + "2774": "uste", + "2775": "emme", + "2776": "idad", + "2777": " пла", + "2778": "jat", + "2779": "при", + "2780": "miss", + "2781": " kv", + "2782": "ski", + "2783": " will", + "2784": " hand", + "2785": "tra", + "2786": " дума", + "2787": " σα", + "2788": " stor", + "2789": "fa", + "2790": " bel", + "2791": "ăm", + "2792": "кла", + "2793": " għall", + "2794": " hi", + "2795": " tele", + "2796": "ché", + "2797": " kdy", + "2798": "EEEE", + "2799": "ється", + "2800": " did", + "2801": " Takže", + "2802": " dist", + "2803": "λέ", + "2804": "дя", + "2805": "ный", + "2806": "ovat", + "2807": "ós", + "2808": "ții", + "2809": "cija", + "2810": "tic", + "2811": " dej", + "2812": "вите", + "2813": " cr", + "2814": "itet", + "2815": " chi", + "2816": "zione", + "2817": " sed", + "2818": "uss", + "2819": "nud", + "2820": " dag", + "2821": "ations", + "2822": "seb", + "2823": " dov", + "2824": " tan", + "2825": "cep", + "2826": "ție", + "2827": "ίζ", + "2828": " gi", + "2829": "čen", + "2830": " kako", + "2831": "ωω", + "2832": "δο", + "2833": " solo", + "2834": "waż", + "2835": " Ho", + "2836": "iska", + "2837": " spr", + "2838": " né", + "2839": "yr", + "2840": "φο", + "2841": "mod", + "2842": "nom", + "2843": "kas", + "2844": " Sp", + "2845": "hte", + "2846": " just", + "2847": "ċċ", + "2848": "āju", + "2849": " hist", + "2850": " å", + "2851": "ват", + "2852": " faz", + "2853": "onn", + "2854": " тако", + "2855": "он", + "2856": " его", + "2857": " ś", + "2858": " vra", + "2859": " ανα", + "2860": " skulle", + "2861": " mě", + "2862": " aqu", + "2863": " bra", + "2864": "vä", + "2865": " know", + "2866": "lio", + "2867": "ula", + "2868": "aja", + "2869": "сту", + "2870": " On", + "2871": " Co", + "2872": " cin", + "2873": "alis", + "2874": " hier", + "2875": "vir", + "2876": " asi", + "2877": "īj", + "2878": " já", + "2879": " hát", + "2880": " sor", + "2881": "iva", + "2882": " gener", + "2883": " mond", + "2884": "ότε", + "2885": "lat", + "2886": "ним", + "2887": " tul", + "2888": " hemm", + "2889": "κό", + "2890": "ild", + "2891": " πολύ", + "2892": " door", + "2893": "esp", + "2894": " ook", + "2895": " joka", + "2896": " sou", + "2897": "bor", + "2898": "zą", + "2899": " ком", + "2900": "esch", + "2901": "ator", + "2902": " esc", + "2903": "пер", + "2904": " wię", + "2905": " Hän", + "2906": "γο", + "2907": " ä", + "2908": "ovo", + "2909": " În", + "2910": " же", + "2911": " nev", + "2912": "hekk", + "2913": " zna", + "2914": " Fran", + "2915": "hing", + "2916": " ace", + "2917": " EU", + "2918": " dei", + "2919": "νέ", + "2920": "ől", + "2921": " Це", + "2922": " zač", + "2923": " år", + "2924": "gli", + "2925": "кра", + "2926": " ту", + "2927": " nii", + "2928": " prom", + "2929": " hom", + "2930": "uta", + "2931": "ется", + "2932": " Az", + "2933": "чки", + "2934": "rop", + "2935": "arte", + "2936": " eff", + "2937": "ħħ", + "2938": "être", + "2939": "sto", + "2940": " Та", + "2941": " vend", + "2942": " mida", + "2943": " bas", + "2944": " über", + "2945": " Mas", + "2946": " ed", + "2947": " več", + "2948": "ungen", + "2949": "τικά", + "2950": " aš", + "2951": " Der", + "2952": " коли", + "2953": " Γ", + "2954": "бва", + "2955": "ód", + "2956": "ικά", + "2957": " Bo", + "2958": " pet", + "2959": "ep", + "2960": "ποι", + "2961": "cut", + "2962": "ič", + "2963": "łem", + "2964": " kans", + "2965": "imas", + "2966": " само", + "2967": "bal", + "2968": "uda", + "2969": " ret", + "2970": " Nem", + "2971": "oss", + "2972": " när", + "2973": "ρή", + "2974": " tad", + "2975": " ovat", + "2976": " αλλά", + "2977": "ște", + "2978": "kar", + "2979": " niin", + "2980": " viņš", + "2981": "ljen", + "2982": " κάν", + "2983": " trans", + "2984": "ību", + "2985": " vain", + "2986": " Это", + "2987": " pří", + "2988": "ču", + "2989": " kul", + "2990": "ques", + "2991": " zas", + "2992": " они", + "2993": "atud", + "2994": "fici", + "2995": "ikt", + "2996": " його", + "2997": " video", + "2998": " disse", + "2999": " ins", + "3000": " Pa", + "3001": "ea", + "3002": "ūt", + "3003": " film", + "3004": "uw", + "3005": " via", + "3006": "änd", + "3007": " Ge", + "3008": "ines", + "3009": "рябва", + "3010": "anda", + "3011": "tal", + "3012": "agi", + "3013": " вони", + "3014": "chod", + "3015": " kaž", + "3016": " tego", + "3017": "клю", + "3018": " pan", + "3019": "rd", + "3020": "mal", + "3021": "amis", + "3022": " kli", + "3023": " muito", + "3024": " set", + "3025": " εξ", + "3026": "ции", + "3027": "вър", + "3028": " från", + "3029": "nym", + "3030": "kaz", + "3031": "nog", + "3032": "bra", + "3033": "íte", + "3034": "ке", + "3035": " jie", + "3036": " lig", + "3037": "ъде", + "3038": "ump", + "3039": " beh", + "3040": "ēja", + "3041": " dwar", + "3042": " jeho", + "3043": "pra", + "3044": " quel", + "3045": " три", + "3046": " fun", + "3047": "itate", + "3048": "emente", + "3049": "zzjoni", + "3050": "rei", + "3051": " ζ", + "3052": "lut", + "3053": "oto", + "3054": "har", + "3055": " Και", + "3056": "ře", + "3057": "ред", + "3058": " Sa", + "3059": " köz", + "3060": " või", + "3061": " które", + "3062": "вали", + "3063": " super", + "3064": " ļ", + "3065": "ża", + "3066": " Por", + "3067": "uti", + "3068": " доб", + "3069": "ções", + "3070": " Так", + "3071": "ży", + "3072": "inte", + "3073": " ні", + "3074": "able", + "3075": "reg", + "3076": " luc", + "3077": "ικό", + "3078": " At", + "3079": " á", + "3080": " deze", + "3081": "tid", + "3082": " turi", + "3083": "urde", + "3084": " оста", + "3085": " neb", + "3086": "nic", + "3087": " Dar", + "3088": " sist", + "3089": "nim", + "3090": " nel", + "3091": "car", + "3092": " euro", + "3093": "yan", + "3094": " aff", + "3095": "urt", + "3096": "три", + "3097": " vast", + "3098": "gr", + "3099": "ivo", + "3100": " blev", + "3101": "AA", + "3102": " які", + "3103": "čiau", + "3104": " Min", + "3105": "amp", + "3106": " което", + "3107": " zm", + "3108": " For", + "3109": " from", + "3110": " most", + "3111": " mir", + "3112": "που", + "3113": " amely", + "3114": "она", + "3115": " mô", + "3116": "ische", + "3117": " einen", + "3118": "sim", + "3119": " tym", + "3120": " Tas", + "3121": " nim", + "3122": " два", + "3123": " aber", + "3124": "мер", + "3125": " comme", + "3126": " zem", + "3127": "xt", + "3128": "lie", + "3129": "iki", + "3130": "ар", + "3131": "íme", + "3132": " tien", + "3133": "enden", + "3134": "fel", + "3135": "yen", + "3136": " inform", + "3137": " rend", + "3138": "ċi", + "3139": "dan", + "3140": " чу", + "3141": " cil", + "3142": "cket", + "3143": " util", + "3144": "aga", + "3145": " Per", + "3146": "ασ", + "3147": " sä", + "3148": "ция", + "3149": " Ž", + "3150": " zab", + "3151": " cos", + "3152": "żda", + "3153": " Ми", + "3154": " jew", + "3155": " tout", + "3156": " vēl", + "3157": "idade", + "3158": "zo", + "3159": " strat", + "3160": " dir", + "3161": " bila", + "3162": "itar", + "3163": "eket", + "3164": "ello", + "3165": "port", + "3166": "uten", + "3167": "δε", + "3168": " Su", + "3169": "uns", + "3170": "eft", + "3171": " fog", + "3172": " Ah", + "3173": " voi", + "3174": "hy", + "3175": "fen", + "3176": "śli", + "3177": " zak", + "3178": "iel", + "3179": "єте", + "3180": "erne", + "3181": " слу", + "3182": " kuin", + "3183": "iny", + "3184": " være", + "3185": "πω", + "3186": "ants", + "3187": " того", + "3188": "ього", + "3189": " nach", + "3190": " mutta", + "3191": "amb", + "3192": " oder", + "3193": " пер", + "3194": " Non", + "3195": "æn", + "3196": "itel", + "3197": " slu", + "3198": " Те", + "3199": "ellen", + "3200": "omen", + "3201": " svoj", + "3202": "ajú", + "3203": "вле", + "3204": " auch", + "3205": "umi", + "3206": "għu", + "3207": "out", + "3208": "chi", + "3209": " wird", + "3210": " ktoré", + "3211": " Das", + "3212": " jej", + "3213": " Amer", + "3214": "śmy", + "3215": "jek", + "3216": "klad", + "3217": " koh", + "3218": "pä", + "3219": "lag", + "3220": " Hu", + "3221": "utt", + "3222": "vent", + "3223": "ēm", + "3224": " loc", + "3225": " zin", + "3226": " але", + "3227": "tää", + "3228": " kaik", + "3229": " ima", + "3230": "over", + "3231": "μη", + "3232": "stru", + "3233": "vien", + "3234": "amas", + "3235": "isse", + "3236": "amen", + "3237": "ivat", + "3238": "áci", + "3239": "žen", + "3240": " acc", + "3241": "ode", + "3242": " entre", + "3243": " ville", + "3244": " sar", + "3245": "τική", + "3246": "ester", + "3247": " You", + "3248": " hade", + "3249": "taj", + "3250": " sti", + "3251": "tit", + "3252": " ties", + "3253": " Tr", + "3254": " tiegħu", + "3255": "nem", + "3256": "тому", + "3257": "obb", + "3258": " taf", + "3259": "vá", + "3260": " Ka", + "3261": "abil", + "3262": " kao", + "3263": " mens", + "3264": " Mis", + "3265": "iver", + "3266": " Dan", + "3267": "oga", + "3268": "ví", + "3269": "вать", + "3270": "hol", + "3271": " cli", + "3272": "han", + "3273": " pris", + "3274": "cher", + "3275": "кі", + "3276": "okat", + "3277": "jav", + "3278": "ted", + "3279": "cc", + "3280": " Ф", + "3281": "str", + "3282": " publi", + "3283": " nom", + "3284": "ną", + "3285": " our", + "3286": " skal", + "3287": "ové", + "3288": "ske", + "3289": "för", + "3290": "etti", + "3291": " vie", + "3292": " št", + "3293": " Con", + "3294": " lav", + "3295": "sak", + "3296": "kur", + "3297": " prob", + "3298": "ork", + "3299": "öt", + "3300": " keď", + "3301": " viņa", + "3302": "έρ", + "3303": " redu", + "3304": "odo", + "3305": "fr", + "3306": " sho", + "3307": " sve", + "3308": "chten", + "3309": " tror", + "3310": " arb", + "3311": " žel", + "3312": " dro", + "3313": "ματα", + "3314": " трябва", + "3315": "ves", + "3316": " tiet", + "3317": " Това", + "3318": "abb", + "3319": "ник", + "3320": " tiek", + "3321": "νω", + "3322": " bus", + "3323": " main", + "3324": " зро", + "3325": "inter", + "3326": "adas", + "3327": "ită", + "3328": "rai", + "3329": "ital", + "3330": " jsou", + "3331": "ób", + "3332": " få", + "3333": " вси", + "3334": "ця", + "3335": "amme", + "3336": " без", + "3337": " kir", + "3338": " jums", + "3339": "rž", + "3340": "хи", + "3341": " Ro", + "3342": " svoje", + "3343": "īg", + "3344": " pil", + "3345": " mie", + "3346": " ras", + "3347": "lin", + "3348": " тому", + "3349": " Ei", + "3350": " kann", + "3351": "īgi", + "3352": " foi", + "3353": " pow", + "3354": "éd", + "3355": "rze", + "3356": " laik", + "3357": " there", + "3358": " lie", + "3359": "amat", + "3360": " todos", + "3361": " ul", + "3362": " dou", + "3363": " Ku", + "3364": " informa", + "3365": "ked", + "3366": "ári", + "3367": "ētu", + "3368": " mint", + "3369": "λι", + "3370": " Les", + "3371": "kus", + "3372": "azione", + "3373": " prí", + "3374": " été", + "3375": " пот", + "3376": "лько", + "3377": " tudi", + "3378": " läh", + "3379": "olu", + "3380": "elijk", + "3381": " dod", + "3382": " mö", + "3383": "τητα", + "3384": " Ter", + "3385": " bħal", + "3386": " ση", + "3387": "ných", + "3388": "će", + "3389": " lan", + "3390": "cem", + "3391": " dla", + "3392": " pero", + "3393": "gar", + "3394": "chte", + "3395": "ents", + "3396": " vores", + "3397": "illä", + "3398": "ogra", + "3399": " které", + "3400": "enie", + "3401": " lin", + "3402": " kind", + "3403": "lament", + "3404": " когда", + "3405": "oup", + "3406": " dot", + "3407": " asta", + "3408": " X", + "3409": " fait", + "3410": " need", + "3411": " напра", + "3412": "ovať", + "3413": " Har", + "3414": " mid", + "3415": " fat", + "3416": " mel", + "3417": "iji", + "3418": " veel", + "3419": "ġa", + "3420": " sai", + "3421": "zie", + "3422": "ciones", + "3423": "kār", + "3424": " hen", + "3425": "tura", + "3426": " drž", + "3427": " Pri", + "3428": " nou", + "3429": " ner", + "3430": "ris", + "3431": " azt", + "3432": " wenn", + "3433": "ром", + "3434": "μό", + "3435": " Ele", + "3436": " сказа", + "3437": "рт", + "3438": " aktar", + "3439": " Em", + "3440": " rez", + "3441": " cum", + "3442": "edi", + "3443": "τού", + "3444": "тися", + "3445": " bude", + "3446": "heid", + "3447": " Ak", + "3448": " tol", + "3449": "ан", + "3450": " bek", + "3451": "δα", + "3452": "juk", + "3453": "nia", + "3454": " bara", + "3455": "ому", + "3456": " mult", + "3457": "ellä", + "3458": "ribu", + "3459": "ister", + "3460": " Wir", + "3461": "så", + "3462": " alebo", + "3463": "mat", + "3464": "idän", + "3465": " gli", + "3466": "kst", + "3467": "else", + "3468": "vers", + "3469": " prod", + "3470": " what", + "3471": " Εί", + "3472": " jih", + "3473": "ction", + "3474": " princip", + "3475": " ao", + "3476": " conc", + "3477": "ában", + "3478": "lem", + "3479": " боль", + "3480": " real", + "3481": "τυ", + "3482": "ным", + "3483": " John", + "3484": " meget", + "3485": " ili", + "3486": " more", + "3487": " kif", + "3488": " sit", + "3489": "antes", + "3490": " vara", + "3491": " lil", + "3492": " bardz", + "3493": "ejn", + "3494": " get", + "3495": " nebo", + "3496": " jā", + "3497": " Pre", + "3498": " Ab", + "3499": " zb", + "3500": "ujem", + "3501": "imą", + "3502": "jan", + "3503": " bis", + "3504": "вет", + "3505": " teh", + "3506": " ning", + "3507": " Ko", + "3508": " if", + "3509": " tagħna", + "3510": " syst", + "3511": "għa", + "3512": "tigt", + "3513": " aiz", + "3514": "kor", + "3515": " över", + "3516": "сля", + "3517": "szyst", + "3518": "SS", + "3519": "γα", + "3520": "kaan", + "3521": "elli", + "3522": " гра", + "3523": "jar", + "3524": " seu", + "3525": "ρισ", + "3526": " segu", + "3527": " hans", + "3528": "κο", + "3529": "iju", + "3530": " tied", + "3531": "ję", + "3532": "jas", + "3533": " Mi", + "3534": " тя", + "3535": " Ad", + "3536": " hod", + "3537": "μβ", + "3538": " θέ", + "3539": "hren", + "3540": " nez", + "3541": "рен", + "3542": " пи", + "3543": " zam", + "3544": " resp", + "3545": " ban", + "3546": " наши", + "3547": "hts", + "3548": "nou", + "3549": "žit", + "3550": " bru", + "3551": " Car", + "3552": " Ind", + "3553": " proces", + "3554": "ța", + "3555": "uo", + "3556": "фи", + "3557": "pat", + "3558": " Mais", + "3559": " všet", + "3560": "ksi", + "3561": "tern", + "3562": " män", + "3563": "ρέπει", + "3564": " вас", + "3565": "cti", + "3566": "zem", + "3567": " exist", + "3568": " kil", + "3569": "ake", + "3570": "iro", + "3571": "tain", + "3572": "jt", + "3573": "adi", + "3574": "фор", + "3575": "ling", + "3576": " gos", + "3577": " heeft", + "3578": " nuest", + "3579": "šč", + "3580": "aut", + "3581": " Το", + "3582": " plat", + "3583": " mijn", + "3584": " også", + "3585": " před", + "3586": "kli", + "3587": " rig", + "3588": " bre", + "3589": " ус", + "3590": "ző", + "3591": "ign", + "3592": " But", + "3593": " Ent", + "3594": " mí", + "3595": " ві", + "3596": "itas", + "3597": " hyv", + "3598": " vre", + "3599": " άλλ", + "3600": " essere", + "3601": "jal", + "3602": "υτό", + "3603": " forma", + "3604": "ogħ", + "3605": "mä", + "3606": "σουμε", + "3607": " saj", + "3608": " Ex", + "3609": " hon", + "3610": " jer", + "3611": " když", + "3612": " kül", + "3613": "зво", + "3614": "izi", + "3615": " test", + "3616": " Do", + "3617": " spre", + "3618": "chu", + "3619": "ємо", + "3620": "illi", + "3621": " moment", + "3622": " üt", + "3623": " port", + "3624": "pēc", + "3625": " επί", + "3626": " doll", + "3627": "asz", + "3628": "nego", + "3629": " your", + "3630": " biz", + "3631": " hem", + "3632": " aș", + "3633": " полу", + "3634": " fort", + "3635": "нии", + "3636": "lijk", + "3637": " stat", + "3638": "oso", + "3639": " face", + "3640": " біль", + "3641": " jekk", + "3642": " grande", + "3643": "ph", + "3644": " porque", + "3645": "ħa", + "3646": "lep", + "3647": " bę", + "3648": " Dob", + "3649": "öra", + "3650": " land", + "3651": "ilt", + "3652": "ėti", + "3653": "мы", + "3654": " mums", + "3655": " njeg", + "3656": " biti", + "3657": "vé", + "3658": " Od", + "3659": "biamo", + "3660": " anche", + "3661": "rot", + "3662": "рез", + "3663": " ó", + "3664": "ajā", + "3665": " tw", + "3666": "egen", + "3667": "isia", + "3668": " Ste", + "3669": "θεί", + "3670": "бле", + "3671": " czy", + "3672": " ec", + "3673": " кой", + "3674": "ikor", + "3675": " ani", + "3676": " дол", + "3677": "add", + "3678": " când", + "3679": "ūr", + "3680": " κατα", + "3681": " worden", + "3682": "lam", + "3683": " vas", + "3684": " прави", + "3685": " stra", + "3686": "jonijiet", + "3687": " És", + "3688": "itiv", + "3689": "szer", + "3690": "lov", + "3691": " tema", + "3692": " cette", + "3693": " eks", + "3694": " tor", + "3695": "voj", + "3696": " vě", + "3697": " bem", + "3698": "ск", + "3699": " compl", + "3700": "mm", + "3701": "prav", + "3702": " fer", + "3703": " hac", + "3704": " sat", + "3705": "тся", + "3706": " Ка", + "3707": " jin", + "3708": " zad", + "3709": "jor", + "3710": " Eu", + "3711": " sy", + "3712": "ener", + "3713": "jak", + "3714": "rib", + "3715": " gdy", + "3716": "zet", + "3717": "vin", + "3718": " ők", + "3719": "фе", + "3720": "ight", + "3721": "öd", + "3722": " mycket", + "3723": "ază", + "3724": " ø", + "3725": "ple", + "3726": "тра", + "3727": " fir", + "3728": "чески", + "3729": " lug", + "3730": "nan", + "3731": "éri", + "3732": "etta", + "3733": " zar", + "3734": "бли", + "3735": " ээ", + "3736": " jär", + "3737": " pare", + "3738": " Lu", + "3739": "ká", + "3740": "eden", + "3741": " bor", + "3742": " был", + "3743": " eles", + "3744": "ύτε", + "3745": "дь", + "3746": " ħa", + "3747": " dav", + "3748": " dacă", + "3749": " Au", + "3750": " natur", + "3751": "lt", + "3752": " Ac", + "3753": " molto", + "3754": " Br", + "3755": " glob", + "3756": " stand", + "3757": "ετε", + "3758": "τσι", + "3759": " απ", + "3760": " kw", + "3761": "kej", + "3762": " bal", + "3763": "urile", + "3764": "nis", + "3765": " kak", + "3766": " mad", + "3767": " nak", + "3768": " viet", + "3769": " mă", + "3770": " lang", + "3771": "iesz", + "3772": " nis", + "3773": "óó", + "3774": " gad", + "3775": " daudz", + "3776": " pros", + "3777": " toho", + "3778": "του", + "3779": " zg", + "3780": " oper", + "3781": " olen", + "3782": ",-", + "3783": "lav", + "3784": " zat", + "3785": " põ", + "3786": "ъм", + "3787": " ľ", + "3788": "ħseb", + "3789": "šen", + "3790": "ких", + "3791": " στα", + "3792": " best", + "3793": " fant", + "3794": " tempo", + "3795": "ġi", + "3796": " Мо", + "3797": " hor", + "3798": " était", + "3799": " timp", + "3800": "eder", + "3801": "ål", + "3802": " stan", + "3803": " andere", + "3804": " tamb", + "3805": " zw", + "3806": " meta", + "3807": " nije", + "3808": "ség", + "3809": "kä", + "3810": "νό", + "3811": "доб", + "3812": "съ", + "3813": " È", + "3814": "ды", + "3815": "šie", + "3816": " Але", + "3817": " myös", + "3818": " προσ", + "3819": " ord", + "3820": " skup", + "3821": "air", + "3822": "bién", + "3823": " след", + "3824": "ogu", + "3825": " pret", + "3826": " acest", + "3827": " той", + "3828": "čo", + "3829": " Med", + "3830": "urn", + "3831": " Ni", + "3832": "ence", + "3833": "ster", + "3834": " kienet", + "3835": "laus", + "3836": " jsme", + "3837": " mano", + "3838": "kan", + "3839": "kem", + "3840": " Á", + "3841": " hum", + "3842": " olla", + "3843": "ίο", + "3844": "ной", + "3845": "ком", + "3846": " viņi", + "3847": " Gr", + "3848": " kü", + "3849": " dien", + "3850": " осно", + "3851": " também", + "3852": " δύ", + "3853": " vše", + "3854": "вро", + "3855": " csak", + "3856": "sob", + "3857": " ļoti", + "3858": " Her", + "3859": " gå", + "3860": "тов", + "3861": "úú", + "3862": " Za", + "3863": " desc", + "3864": "amy", + "3865": "rob", + "3866": " där", + "3867": "niej", + "3868": " Hij", + "3869": " дуже", + "3870": "ender", + "3871": "aje", + "3872": " tij", + "3873": " време", + "3874": "elis", + "3875": " soci", + "3876": " sj", + "3877": " който", + "3878": "ké", + "3879": " Mor", + "3880": " știi", + "3881": " haar", + "3882": "col", + "3883": " apr", + "3884": "ondol", + "3885": "konom", + "3886": "бира", + "3887": "ningen", + "3888": "στη", + "3889": " ans", + "3890": "ulla", + "3891": "roj", + "3892": " μπορεί", + "3893": "tea", + "3894": " fakt", + "3895": "hor", + "3896": "azzjoni", + "3897": "ými", + "3898": " waren", + "3899": "ró", + "3900": " primer", + "3901": " gali", + "3902": " energ", + "3903": "μή", + "3904": " есть", + "3905": "раз", + "3906": " sõ", + "3907": " hur", + "3908": "īs", + "3909": "rag", + "3910": "üüd", + "3911": " ses", + "3912": " можете", + "3913": "ęs", + "3914": " Tā", + "3915": "μένο", + "3916": " ly", + "3917": "stav", + "3918": " sest", + "3919": " vál", + "3920": " Ob", + "3921": "usti", + "3922": "ycz", + "3923": "αααα", + "3924": " off", + "3925": " loro", + "3926": " πρό", + "3927": "anud", + "3928": "iera", + "3929": " proto", + "3930": "ham", + "3931": "ley", + "3932": " prá", + "3933": " about", + "3934": " questi", + "3935": "ekl", + "3936": " vam", + "3937": "pport", + "3938": "νή", + "3939": " ber", + "3940": " apie", + "3941": " moh", + "3942": "ņem", + "3943": "мя", + "3944": " няко", + "3945": " term", + "3946": " пі", + "3947": " cal", + "3948": " signi", + "3949": "pan", + "3950": "вся", + "3951": "нима", + "3952": " было", + "3953": "odi", + "3954": "ného", + "3955": " todo", + "3956": "jel", + "3957": " hä", + "3958": "δια", + "3959": "eng", + "3960": "стро", + "3961": "iß", + "3962": "ώρα", + "3963": " osob", + "3964": "ást", + "3965": "vy", + "3966": " inim", + "3967": " meie", + "3968": "vas", + "3969": "ħra", + "3970": " Δεν", + "3971": " також", + "3972": " wszyst", + "3973": " kle", + "3974": " Maar", + "3975": "ischen", + "3976": "ück", + "3977": " говори", + "3978": "ail", + "3979": "cije", + "3980": "ката", + "3981": "isesti", + "3982": "ød", + "3983": "atie", + "3984": " olyan", + "3985": "ρία", + "3986": "šanas", + "3987": "кт", + "3988": " verd", + "3989": "bes", + "3990": "ött", + "3991": " fal", + "3992": " ça", + "3993": " famil", + "3994": " poved", + "3995": " Sim", + "3996": " га", + "3997": "udi", + "3998": "γκ", + "3999": " Bar", + "4000": "iksi", + "4001": "oug", + "4002": "пол", + "4003": " Col", + "4004": " który", + "4005": " lõ", + "4006": "otre", + "4007": " pode", + "4008": "ára", + "4009": " Bu", + "4010": " Jis", + "4011": " koje", + "4012": " faire", + "4013": "гля", + "4014": "вання", + "4015": "ced", + "4016": " grand", + "4017": "hoz", + "4018": "жли", + "4019": " oč", + "4020": " Was", + "4021": "oon", + "4022": "ört", + "4023": " eso", + "4024": " conf", + "4025": " Ви", + "4026": " pove", + "4027": "ario", + "4028": " nagu", + "4029": " cilvē", + "4030": " hj", + "4031": "lic", + "4032": "чі", + "4033": " ці", + "4034": "inden", + "4035": "jd", + "4036": " sh", + "4037": " igen", + "4038": "τω", + "4039": "щото", + "4040": " tech", + "4041": " eh", + "4042": " Pero", + "4043": " tav", + "4044": " hvor", + "4045": "gg", + "4046": " také", + "4047": " abs", + "4048": " которые", + "4049": "žu", + "4050": "zę", + "4051": "лся", + "4052": " kop", + "4053": " können", + "4054": " miss", + "4055": "шу", + "4056": "jekt", + "4057": " παρα", + "4058": " було", + "4059": "itos", + "4060": "ále", + "4061": "abe", + "4062": " пу", + "4063": "ré", + "4064": "dzi", + "4065": " καθ", + "4066": " други", + "4067": " dak", + "4068": " nur", + "4069": " Lo", + "4070": " ye", + "4071": " jien", + "4072": " ř", + "4073": "ady", + "4074": " prim", + "4075": "iske", + "4076": "egu", + "4077": "рів", + "4078": " než", + "4079": " pier", + "4080": " vair", + "4081": "έν", + "4082": " Он", + "4083": "ieni", + "4084": " rest", + "4085": "dom", + "4086": " чо", + "4087": "ști", + "4088": "jiet", + "4089": " ang", + "4090": "змо", + "4091": " Aber", + "4092": " когато", + "4093": "of", + "4094": "kat", + "4095": " або", + "4096": "dje", + "4097": " él", + "4098": "ên", + "4099": "дей", + "4100": " mů", + "4101": " пе", + "4102": " preto", + "4103": " mno", + "4104": "bie", + "4105": "žete", + "4106": " έχει", + "4107": "kti", + "4108": "kså", + "4109": "stä", + "4110": " compr", + "4111": "кри", + "4112": "act", + "4113": " ref", + "4114": " був", + "4115": " habe", + "4116": " Він", + "4117": " wurde", + "4118": " myslím", + "4119": "ares", + "4120": " också", + "4121": "aties", + "4122": " byl", + "4123": " pit", + "4124": " delle", + "4125": "ube", + "4126": "otta", + "4127": " ple", + "4128": " word", + "4129": " вам", + "4130": " toim", + "4131": "ția", + "4132": " mūsu", + "4133": "uno", + "4134": " foarte", + "4135": " Θ", + "4136": " bare", + "4137": " знаете", + "4138": " ру", + "4139": " são", + "4140": " svar", + "4141": "ford", + "4142": "yw", + "4143": "æl", + "4144": "ody", + "4145": " être", + "4146": "øre", + "4147": "ures", + "4148": "jų", + "4149": " bio", + "4150": " τε", + "4151": " spo", + "4152": " rá", + "4153": "cas", + "4154": " denne", + "4155": " weet", + "4156": "dat", + "4157": "tes", + "4158": "usa", + "4159": " înt", + "4160": "ούν", + "4161": " mere", + "4162": " muut", + "4163": "arg", + "4164": " ευ", + "4165": " now", + "4166": " labai", + "4167": "öv", + "4168": "iner", + "4169": "jev", + "4170": " einem", + "4171": "ībā", + "4172": " vă", + "4173": " pel", + "4174": "gra", + "4175": "roz", + "4176": " rów", + "4177": " twe", + "4178": "čas", + "4179": " фи", + "4180": " zaj", + "4181": "liga", + "4182": "дите", + "4183": " ham", + "4184": "maz", + "4185": "nolog", + "4186": "ivel", + "4187": "oko", + "4188": "πάρχ", + "4189": " bin", + "4190": " Gre", + "4191": " prac", + "4192": "ovali", + "4193": "urg", + "4194": " Sh", + "4195": "chter", + "4196": "ním", + "4197": "stu", + "4198": "ров", + "4199": " Во", + "4200": " dobro", + "4201": " onder", + "4202": " organiz", + "4203": "zna", + "4204": " Quindi", + "4205": "sit", + "4206": " vse", + "4207": "ace", + "4208": " syn", + "4209": " szám", + "4210": " tej", + "4211": " casa", + "4212": "oved", + "4213": " minden", + "4214": "ay", + "4215": "atu", + "4216": " rob", + "4217": " deux", + "4218": "rom", + "4219": " sap", + "4220": "ój", + "4221": "бі", + "4222": " pus", + "4223": " would", + "4224": "сті", + "4225": " algo", + "4226": "lap", + "4227": " før", + "4228": " ges", + "4229": " dette", + "4230": "ppen", + "4231": "iausi", + "4232": " cri", + "4233": "riv", + "4234": " този", + "4235": " їх", + "4236": " vaik", + "4237": " cela", + "4238": "pla", + "4239": " cred", + "4240": "ху", + "4241": "essä", + "4242": "intre", + "4243": " onze", + "4244": "osto", + "4245": " fig", + "4246": " Nous", + "4247": "γά", + "4248": " думаю", + "4249": "nas", + "4250": " were", + "4251": "ică", + "4252": "ssä", + "4253": " Ό", + "4254": "idi", + "4255": "lura", + "4256": "σουν", + "4257": " drug", + "4258": "ree", + "4259": " οπο", + "4260": "том", + "4261": "εται", + "4262": " stato", + "4263": " gondol", + "4264": "cji", + "4265": " two", + "4266": "ші", + "4267": " sto", + "4268": " roku", + "4269": "din", + "4270": "rer", + "4271": "ivät", + "4272": "atta", + "4273": "pis", + "4274": " Fl", + "4275": " име", + "4276": " μπορού", + "4277": " deres", + "4278": " dell", + "4279": " kunnen", + "4280": "igi", + "4281": "μαι", + "4282": "ällä", + "4283": " njih", + "4284": "дела", + "4285": " Go", + "4286": "oria", + "4287": " fut", + "4288": "abar", + "4289": " visi", + "4290": "ъъ", + "4291": "ota", + "4292": "țin", + "4293": " Di", + "4294": " vill", + "4295": " tagħhom", + "4296": "ļu", + "4297": " ftit", + "4298": " Mutta", + "4299": "ności", + "4300": "ijos", + "4301": " pesso", + "4302": "īv", + "4303": " lát", + "4304": " diese", + "4305": "öl", + "4306": "ží", + "4307": "стве", + "4308": " odp", + "4309": " pole", + "4310": "φα", + "4311": "enia", + "4312": " Ein", + "4313": " hänen", + "4314": "akse", + "4315": "vän", + "4316": " áll", + "4317": "ême", + "4318": "enas", + "4319": "bin", + "4320": " Os", + "4321": " think", + "4322": " nod", + "4323": "ary", + "4324": "sel", + "4325": "ions", + "4326": " dėl", + "4327": "bat", + "4328": "cze", + "4329": " dva", + "4330": " suur", + "4331": " oss", + "4332": " prv", + "4333": " which", + "4334": " realmente", + "4335": "nú", + "4336": "buie", + "4337": " exper", + "4338": " despre", + "4339": "ξη", + "4340": " kit", + "4341": "oit", + "4342": "ws", + "4343": "ăt", + "4344": "λό", + "4345": " mūsų", + "4346": " Taigi", + "4347": " lim", + "4348": " san", + "4349": " treb", + "4350": "тер", + "4351": "ric", + "4352": " Ма", + "4353": "hel", + "4354": "ība", + "4355": " bý", + "4356": " Als", + "4357": " esto", + "4358": "pas", + "4359": "vait", + "4360": " gew", + "4361": "astă", + "4362": " Ω", + "4363": " uv", + "4364": " vám", + "4365": " tiegħi", + "4366": "ron", + "4367": " иска", + "4368": "áb", + "4369": " их", + "4370": "gram", + "4371": " naud", + "4372": " speci", + "4373": " zav", + "4374": " tren", + "4375": "тель", + "4376": "iser", + "4377": " minut", + "4378": "κι", + "4379": " einer", + "4380": " kõik", + "4381": " ци", + "4382": " daž", + "4383": " съм", + "4384": " him", + "4385": " memb", + "4386": "зы", + "4387": "cin", + "4388": "esen", + "4389": "nega", + "4390": "oten", + "4391": " cosa", + "4392": "δώ", + "4393": " κατά", + "4394": "kkor", + "4395": " lehet", + "4396": " their", + "4397": "itor", + "4398": " havde", + "4399": " pir", + "4400": " αυ", + "4401": "стра", + "4402": " bine", + "4403": " Ke", + "4404": " Som", + "4405": " sano", + "4406": "lia", + "4407": " time", + "4408": "esc", + "4409": " Tom", + "4410": " käyt", + "4411": "гла", + "4412": " jau", + "4413": " vás", + "4414": "dar", + "4415": " bene", + "4416": "fs", + "4417": "κολ", + "4418": " väga", + "4419": "hem", + "4420": " nego", + "4421": " them", + "4422": " tule", + "4423": "zik", + "4424": "eter", + "4425": "zin", + "4426": " chc", + "4427": " palju", + "4428": "nosť", + "4429": "aba", + "4430": "ird", + "4431": "nad", + "4432": "cej", + "4433": " пове", + "4434": "egi", + "4435": " ω", + "4436": "som", + "4437": " ką", + "4438": " meer", + "4439": " ден", + "4440": "υσ", + "4441": "ské", + "4442": " сме", + "4443": "rok", + "4444": "ίνει", + "4445": " uno", + "4446": " wel", + "4447": " senti", + "4448": "qa", + "4449": " помо", + "4450": " jotka", + "4451": "īm", + "4452": "ool", + "4453": " ljudi", + "4454": " ér", + "4455": " lub", + "4456": " υπο", + "4457": " much", + "4458": "teri", + "4459": " aug", + "4460": " ces", + "4461": "ople", + "4462": "tam", + "4463": "line", + "4464": " bei", + "4465": " geen", + "4466": "дат", + "4467": "undo", + "4468": " naz", + "4469": "ючи", + "4470": " üks", + "4471": " ж", + "4472": "tere", + "4473": " ри", + "4474": "erna", + "4475": " sid", + "4476": " πρέπει", + "4477": "шен", + "4478": " эти", + "4479": "ring", + "4480": "стоя", + "4481": " koll", + "4482": " possi", + "4483": " przez", + "4484": " naše", + "4485": "isin", + "4486": " mån", + "4487": " vlast", + "4488": " amb", + "4489": " отно", + "4490": "fik", + "4491": " í", + "4492": " még", + "4493": "зможно", + "4494": "irt", + "4495": "сет", + "4496": " stru", + "4497": " tipo", + "4498": " esper", + "4499": " μό", + "4500": " zou", + "4501": " сте", + "4502": " Той", + "4503": " tit", + "4504": " πρα", + "4505": " тре", + "4506": " Mislim", + "4507": "lé", + "4508": "ök", + "4509": "ittä", + "4510": "ají", + "4511": " там", + "4512": " zal", + "4513": "imal", + "4514": " espe", + "4515": " iesp", + "4516": "ιδ", + "4517": "зе", + "4518": "pak", + "4519": "avez", + "4520": " defin", + "4521": "app", + "4522": " dog", + "4523": " Pe", + "4524": "owie", + "4525": " Mon", + "4526": " Kas", + "4527": " jel", + "4528": " към", + "4529": "лно", + "4530": " од", + "4531": " hvad", + "4532": "gov", + "4533": "isz", + "4534": "owe", + "4535": " люди", + "4536": "ствен", + "4537": " ment", + "4538": " precis", + "4539": "ño", + "4540": " savu", + "4541": " бъде", + "4542": " Sam", + "4543": " тези", + "4544": " selv", + "4545": "com", + "4546": " may", + "4547": "arna", + "4548": " úgy", + "4549": "ujú", + "4550": "ким", + "4551": "anden", + "4552": " Huwa", + "4553": " nagyon", + "4554": "æt", + "4555": " Kui", + "4556": " tista", + "4557": " Pol", + "4558": " Europe", + "4559": " вър", + "4560": " также", + "4561": "tag", + "4562": " muy", + "4563": "нг", + "4564": "isch", + "4565": "рата", + "4566": " some", + "4567": "vod", + "4568": "ibt", + "4569": "pus", + "4570": " сло", + "4571": " Pod", + "4572": "cional", + "4573": "λει", + "4574": " če", + "4575": "rast", + "4576": "чень", + "4577": " red", + "4578": " redz", + "4579": " ktorý", + "4580": " got", + "4581": " mitä", + "4582": "ms", + "4583": "pă", + "4584": "ijas", + "4585": " Então", + "4586": " comun", + "4587": "onna", + "4588": " ihr", + "4589": "прав", + "4590": "мент", + "4591": "ală", + "4592": "jeb", + "4593": " ils", + "4594": " який", + "4595": " Egy", + "4596": " tiegħek", + "4597": " meu", + "4598": "tev", + "4599": " Des", + "4600": "μπ", + "4601": " viel", + "4602": " cost", + "4603": " równie", + "4604": "rac", + "4605": "ків", + "4606": " viņu", + "4607": " kurie", + "4608": "lič", + "4609": " Mu", + "4610": " elő", + "4611": " when", + "4612": " пробле", + "4613": "стви", + "4614": " teraz", + "4615": " Ol", + "4616": "лы", + "4617": "lab", + "4618": "čių", + "4619": "press", + "4620": " fran", + "4621": " Щ", + "4622": " klas", + "4623": "нието", + "4624": " kons", + "4625": " aceste", + "4626": " result", + "4627": " kri", + "4628": " sok", + "4629": " dintre", + "4630": "ęp", + "4631": " který", + "4632": "ψη", + "4633": " kde", + "4634": " ain", + "4635": " zag", + "4636": "бно", + "4637": "lige", + "4638": " nei", + "4639": " noch", + "4640": " popul", + "4641": " Li", + "4642": "пита", + "4643": " Jed", + "4644": " très", + "4645": " годи", + "4646": " dest", + "4647": "rin", + "4648": " want", + "4649": "mun", + "4650": "avat", + "4651": " līdz", + "4652": " ember", + "4653": "hux", + "4654": " hacer", + "4655": " propri", + "4656": "qui", + "4657": " Om", + "4658": " Бо", + "4659": " ast", + "4660": " mislim", + "4661": "has", + "4662": "rea", + "4663": " lok", + "4664": " Pot", + "4665": " Los", + "4666": " Este", + "4667": " δε", + "4668": " difer", + "4669": " autom", + "4670": "ėt", + "4671": " cir", + "4672": " noget", + "4673": " ekonom", + "4674": "adz", + "4675": "yy", + "4676": "ciju", + "4677": " czas", + "4678": " prot", + "4679": " momento", + "4680": "лан", + "4681": " ră", + "4682": " τέ", + "4683": "рите", + "4684": "itte", + "4685": " каза", + "4686": " vär", + "4687": "бити", + "4688": "ones", + "4689": " Val", + "4690": "ieren", + "4691": "ják", + "4692": "ktor", + "4693": " doen", + "4694": " если", + "4695": " lucru", + "4696": "άρ", + "4697": "rač", + "4698": "ół", + "4699": " jas", + "4700": "ільки", + "4701": "xi", + "4702": "poi", + "4703": " буду", + "4704": " ако", + "4705": " transp", + "4706": "ції", + "4707": "atur", + "4708": " дей", + "4709": " doar", + "4710": " bardzo", + "4711": " perché", + "4712": "χα", + "4713": "aux", + "4714": " fot", + "4715": "ście", + "4716": " zur", + "4717": " мене", + "4718": " potreb", + "4719": " zich", + "4720": "igh", + "4721": " pró", + "4722": " York", + "4723": "iog", + "4724": " dus", + "4725": " liet", + "4726": " всички", + "4727": " nen", + "4728": " finns", + "4729": " jeden", + "4730": " mich", + "4731": " amikor", + "4732": "тно", + "4733": " Така", + "4734": " posto", + "4735": " również", + "4736": "pend", + "4737": " cuando", + "4738": " tir", + "4739": "gin", + "4740": " Ger", + "4741": "бра", + "4742": "jes", + "4743": " stad", + "4744": " sik", + "4745": "rzy", + "4746": " há", + "4747": " égal", + "4748": " pes", + "4749": " serv", + "4750": "uz", + "4751": "used", + "4752": " Kun", + "4753": " între", + "4754": "вто", + "4755": "ная", + "4756": "ować", + "4757": "ward", + "4758": " lis", + "4759": " lihts", + "4760": "δή", + "4761": " ami", + "4762": " tän", + "4763": " hely", + "4764": "ador", + "4765": "tais", + "4766": " ons", + "4767": " она", + "4768": "lasti", + "4769": " quer", + "4770": " fazer", + "4771": "хме", + "4772": "idades", + "4773": " hvis", + "4774": " într", + "4775": " Taip", + "4776": " inten", + "4777": "det", + "4778": "σία", + "4779": "aria", + "4780": " pirm", + "4781": " вона", + "4782": "mos", + "4783": " îm", + "4784": " Pok", + "4785": "ξει", + "4786": "ních", + "4787": " ble", + "4788": " ī", + "4789": " работа", + "4790": "кре", + "4791": " past", + "4792": "cause", + "4793": " Λ", + "4794": "ято", + "4795": "jali", + "4796": " Don", + "4797": " she", + "4798": "uer", + "4799": "ænd", + "4800": "тите", + "4801": "bbli", + "4802": " jon", + "4803": " τό", + "4804": "onder", + "4805": " vá", + "4806": " câ", + "4807": " през", + "4808": " notre", + "4809": "ait", + "4810": "ρου", + "4811": "дер", + "4812": "robi", + "4813": " investi", + "4814": "kim", + "4815": "вам", + "4816": "δη", + "4817": " ċ", + "4818": "ées", + "4819": "ээээ", + "4820": " actu", + "4821": " reik", + "4822": " hanno", + "4823": "abel", + "4824": "idos", + "4825": "quer", + "4826": " väh", + "4827": " обра", + "4828": " prote", + "4829": " également", + "4830": "πη", + "4831": " eng", + "4832": " göra", + "4833": " Viņš", + "4834": "нен", + "4835": " än", + "4836": " protože", + "4837": " mik", + "4838": "ily", + "4839": "estä", + "4840": " Mus", + "4841": " priv", + "4842": "ují", + "4843": "zel", + "4844": " prop", + "4845": "βα", + "4846": "ätkö", + "4847": " mensen", + "4848": "dem", + "4849": "νομ", + "4850": "ames", + "4851": " zosta", + "4852": "EEEEEEEE", + "4853": " durante", + "4854": "ao", + "4855": " вико", + "4856": " liber", + "4857": " domāju", + "4858": " Як", + "4859": "ges", + "4860": " αυτή", + "4861": "ása", + "4862": " ξέ", + "4863": " Rob", + "4864": " рас", + "4865": " меня", + "4866": "пі", + "4867": "nit", + "4868": "raj", + "4869": "рова", + "4870": " bro", + "4871": " oprav", + "4872": " До", + "4873": " Tam", + "4874": " ela", + "4875": " kaj", + "4876": " wordt", + "4877": "vr", + "4878": " tě", + "4879": " boli", + "4880": " eg", + "4881": "łu", + "4882": " її", + "4883": " очень", + "4884": "vari", + "4885": " pav", + "4886": " Ve", + "4887": " vaj", + "4888": " avons", + "4889": "etzt", + "4890": "idas", + "4891": " ты", + "4892": "pec", + "4893": " bon", + "4894": " jagħ", + "4895": " pretože", + "4896": " един", + "4897": "ίε", + "4898": " рі", + "4899": "imento", + "4900": "mă", + "4901": "tik", + "4902": "olla", + "4903": " Kat", + "4904": " kre", + "4905": " darb", + "4906": "cat", + "4907": " Οι", + "4908": " been", + "4909": "łe", + "4910": " Você", + "4911": " több", + "4912": " Про", + "4913": "anta", + "4914": "fatt", + "4915": " být", + "4916": "koz", + "4917": "ωωωω", + "4918": " Nä", + "4919": " cen", + "4920": "fica", + "4921": " тази", + "4922": "uses", + "4923": " ziniet", + "4924": "anza", + "4925": "aient", + "4926": "aren", + "4927": "elik", + "4928": " forts", + "4929": " Αυτό", + "4930": " nás", + "4931": " pää", + "4932": " akkor", + "4933": "što", + "4934": " sabe", + "4935": " pär", + "4936": " koska", + "4937": "ника", + "4938": " mehr", + "4939": " него", + "4940": "гле", + "4941": "ajte", + "4942": " rem", + "4943": " якщо", + "4944": " strateg", + "4945": "eksi", + "4946": " lik", + "4947": "voir", + "4948": " prima", + "4949": "fon", + "4950": " būti", + "4951": " even", + "4952": " život", + "4953": " maž", + "4954": " people", + "4955": "τρο", + "4956": " Reg", + "4957": " għa", + "4958": "öm", + "4959": " Chr", + "4960": " πραγμα", + "4961": " Kai", + "4962": " tú", + "4963": "aste", + "4964": " прои", + "4965": "kay", + "4966": " kod", + "4967": " lag", + "4968": " tega", + "4969": " nekaj", + "4970": " Jak", + "4971": " dzie", + "4972": " hekk", + "4973": " Ten", + "4974": " cit", + "4975": "ră", + "4976": "ēju", + "4977": " Va", + "4978": "šim", + "4979": " cur", + "4980": " who", + "4981": "āl", + "4982": " podob", + "4983": " Singapur", + "4984": " Singapore", + "4985": " sli", + "4986": " consider", + "4987": "ład", + "4988": " cui", + "4989": " pis", + "4990": " amer", + "4991": " essa", + "4992": " koja", + "4993": " werd", + "4994": "кой", + "4995": " mä", + "4996": " Ра", + "4997": " Europa", + "4998": " sud", + "4999": "λώ", + "5000": "σαν", + "5001": " гру", + "5002": " więc", + "5003": "kie", + "5004": "stvo", + "5005": " versch", + "5006": "itan", + "5007": " kienu", + "5008": "ними", + "5009": " която", + "5010": "mest", + "5011": "med", + "5012": " malo", + "5013": " υπάρχ", + "5014": " vraiment", + "5015": "alen", + "5016": " aux", + "5017": "kow", + "5018": " мне", + "5019": " importante", + "5020": "ικο", + "5021": " dif", + "5022": " сдела", + "5023": "ya", + "5024": "liv", + "5025": " alc", + "5026": " suo", + "5027": " funk", + "5028": " here", + "5029": " detta", + "5030": "rik", + "5031": "σετε", + "5032": "éc", + "5033": "όν", + "5034": "тели", + "5035": " fav", + "5036": "tige", + "5037": " jen", + "5038": " sek", + "5039": " dim", + "5040": " доста", + "5041": "juč", + "5042": " tiem", + "5043": "σσ", + "5044": " sent", + "5045": " visu", + "5046": " hv", + "5047": " cl", + "5048": "cord", + "5049": " oleks", + "5050": " már", + "5051": "γή", + "5052": "imme", + "5053": " această", + "5054": "ule", + "5055": "υνα", + "5056": " cele", + "5057": " apro", + "5058": " ¡", + "5059": "esa", + "5060": "ман", + "5061": "гі", + "5062": " disk", + "5063": " όταν", + "5064": " άν", + "5065": " suc", + "5066": " use", + "5067": " žm", + "5068": " jedn", + "5069": " nella", + "5070": "cil", + "5071": " -", + "5072": "καν", + "5073": "elor", + "5074": " exc", + "5075": " svet", + "5076": " simpl", + "5077": "χή", + "5078": " δύο", + "5079": " vendar", + "5080": "дов", + "5081": " δη", + "5082": " dzi", + "5083": " donc", + "5084": " kult", + "5085": "nut", + "5086": "áme", + "5087": " им", + "5088": " bħ", + "5089": " над", + "5090": "SA", + "5091": "πί", + "5092": "rim", + "5093": " arm", + "5094": " Как", + "5095": " vairāk", + "5096": " Ú", + "5097": "ots", + "5098": " φο", + "5099": "dział", + "5100": " kuul", + "5101": " kanssa", + "5102": "iert", + "5103": "τρα", + "5104": "minist", + "5105": "oper", + "5106": " ves", + "5107": " modo", + "5108": " tkun", + "5109": " пока", + "5110": "вор", + "5111": "ном", + "5112": "inge", + "5113": " ann", + "5114": " așa", + "5115": " nau", + "5116": " rezult", + "5117": " būt", + "5118": " lev", + "5119": "issen", + "5120": "apt", + "5121": " više", + "5122": "μι", + "5123": " быть", + "5124": "ók", + "5125": " iżda", + "5126": "że", + "5127": "īja", + "5128": " Αλλά", + "5129": "lub", + "5130": " ya", + "5131": " hos", + "5132": " них", + "5133": "istas", + "5134": "ót", + "5135": "fol", + "5136": " κο", + "5137": " ľud", + "5138": "ência", + "5139": " добре", + "5140": "uts", + "5141": " Jah", + "5142": " Ed", + "5143": " suis", + "5144": "čnosti", + "5145": "чай", + "5146": "uter", + "5147": " byť", + "5148": " Tämä", + "5149": " veľmi", + "5150": "ož", + "5151": " Kan", + "5152": " elle", + "5153": " siitä", + "5154": "циа", + "5155": "auc", + "5156": " toh", + "5157": " déc", + "5158": " sei", + "5159": " ħaġa", + "5160": " через", + "5161": "ism", + "5162": " ev", + "5163": " ži", + "5164": " kus", + "5165": "bagħ", + "5166": " vår", + "5167": " nich", + "5168": " econom", + "5169": "ates", + "5170": " doc", + "5171": " gör", + "5172": " tart", + "5173": " ust", + "5174": " vienkār", + "5175": " amit", + "5176": " будет", + "5177": " también", + "5178": "ror", + "5179": "рта", + "5180": "aa", + "5181": "кого", + "5182": " Tod", + "5183": " Tur", + "5184": " był", + "5185": " jäl", + "5186": " нам", + "5187": " вели", + "5188": "qs", + "5189": "ög", + "5190": " trebuie", + "5191": " tin", + "5192": " όπω", + "5193": "ering", + "5194": "рти", + "5195": " pam", + "5196": " στον", + "5197": "rab", + "5198": " šo", + "5199": " tat", + "5200": " gospod", + "5201": "стина", + "5202": " Ih", + "5203": " Não", + "5204": "φή", + "5205": "ách", + "5206": "жна", + "5207": " Dak", + "5208": " dre", + "5209": " mogu", + "5210": "άζ", + "5211": " rod", + "5212": " kollha", + "5213": "šn", + "5214": " vec", + "5215": " každ", + "5216": " dessa", + "5217": "uró", + "5218": "was", + "5219": "elij", + "5220": " bili", + "5221": " sehr", + "5222": "utat", + "5223": "lej", + "5224": " σύ", + "5225": " Jos", + "5226": " bit", + "5227": "ři", + "5228": "elles", + "5229": "pol", + "5230": " sna", + "5231": "ised", + "5232": " Vous", + "5233": " μετα", + "5234": " problema", + "5235": " andra", + "5236": " bun", + "5237": "stand", + "5238": " helt", + "5239": " constru", + "5240": " lihtsalt", + "5241": " log", + "5242": " frem", + "5243": "plo", + "5244": " hoe", + "5245": " into", + "5246": " Ü", + "5247": " ακ", + "5248": "kte", + "5249": " uk", + "5250": "jau", + "5251": " Ce", + "5252": " nyt", + "5253": " πιο", + "5254": " very", + "5255": " цього", + "5256": "ex", + "5257": " soll", + "5258": " going", + "5259": " sempli", + "5260": "ämän", + "5261": " lep", + "5262": " εδώ", + "5263": " välja", + "5264": "sin", + "5265": "тен", + "5266": " gam", + "5267": " Зна", + "5268": " prem", + "5269": " szeret", + "5270": "ést", + "5271": "над", + "5272": "umus", + "5273": " går", + "5274": " jog", + "5275": " ді", + "5276": " vys", + "5277": "ară", + "5278": "ños", + "5279": "jam", + "5280": "zni", + "5281": "inė", + "5282": " ing", + "5283": " Deci", + "5284": " peut", + "5285": " Ph", + "5286": "estu", + "5287": " moet", + "5288": "στα", + "5289": " Or", + "5290": " preg", + "5291": " Мы", + "5292": "obil", + "5293": "țion", + "5294": " запо", + "5295": "ній", + "5296": " My", + "5297": " prez", + "5298": " får", + "5299": " осо", + "5300": " poten", + "5301": "ħed", + "5302": " Се", + "5303": " mok", + "5304": "ivi", + "5305": "pie", + "5306": "iset", + "5307": " täh", + "5308": "зі", + "5309": " siin", + "5310": " один", + "5311": "ную", + "5312": "għid", + "5313": " Mēs", + "5314": " tikai", + "5315": " Ш", + "5316": "rau", + "5317": "зда", + "5318": "кто", + "5319": " Είναι", + "5320": "emi", + "5321": "erea", + "5322": " Kar", + "5323": " στι", + "5324": " вер", + "5325": " Å", + "5326": "äng", + "5327": "ųjų", + "5328": "mien", + "5329": " moje", + "5330": "тів", + "5331": " arv", + "5332": " unter", + "5333": " due", + "5334": " tel", + "5335": " mani", + "5336": "īgs", + "5337": "лаго", + "5338": " дела", + "5339": " tanto", + "5340": " otr", + "5341": " wil", + "5342": " interess", + "5343": " ehm", + "5344": "cker", + "5345": "ds", + "5346": "ful", + "5347": " deb", + "5348": "ungs", + "5349": " σημα", + "5350": "zza", + "5351": "γρα", + "5352": " μη", + "5353": "aszt", + "5354": "ossa", + "5355": " fond", + "5356": "pin", + "5357": " mare", + "5358": " måste", + "5359": "τώ", + "5360": " ги", + "5361": " Char", + "5362": "fü", + "5363": "už", + "5364": "ură", + "5365": "atti", + "5366": "jeme", + "5367": " nagħ", + "5368": " grund", + "5369": " dô", + "5370": " así", + "5371": " disc", + "5372": " компа", + "5373": "ían", + "5374": " hawn", + "5375": " durch", + "5376": " univers", + "5377": " ry", + "5378": " Ako", + "5379": " Bro", + "5380": " then", + "5381": " weiß", + "5382": "bagħad", + "5383": " fă", + "5384": " takže", + "5385": " politi", + "5386": " való", + "5387": "oare", + "5388": " estava", + "5389": "rak", + "5390": " Tu", + "5391": "żi", + "5392": "hmen", + "5393": " interes", + "5394": " ket", + "5395": " mell", + "5396": "ory", + "5397": "rum", + "5398": " minu", + "5399": " ef", + "5400": "fu", + "5401": "kla", + "5402": " ло", + "5403": " regul", + "5404": " its", + "5405": "øl", + "5406": "śl", + "5407": "ème", + "5408": " új", + "5409": "inių", + "5410": " powie", + "5411": " rag", + "5412": " хоро", + "5413": " paljon", + "5414": " tiesiog", + "5415": "heit", + "5416": " veliko", + "5417": "ape", + "5418": "hand", + "5419": " alles", + "5420": " людей", + "5421": " Mad", + "5422": " πρά", + "5423": " všech", + "5424": "mission", + "5425": "adu", + "5426": " Fil", + "5427": " mun", + "5428": " Mc", + "5429": "aff", + "5430": " rom", + "5431": "atori", + "5432": "λω", + "5433": "oden", + "5434": " Áno", + "5435": " noast", + "5436": "avi", + "5437": " kart", + "5438": "cias", + "5439": " bör", + "5440": " vri", + "5441": "ání", + "5442": "itud", + "5443": "onte", + "5444": "ében", + "5445": " ren", + "5446": " час", + "5447": " Miss", + "5448": " fram", + "5449": "duc", + "5450": " vak", + "5451": "iente", + "5452": " επίση", + "5453": "dę", + "5454": "дъл", + "5455": " trab", + "5456": "alu", + "5457": "тив", + "5458": " ří", + "5459": "ture", + "5460": " Tag", + "5461": "гна", + "5462": " prov", + "5463": " encont", + "5464": "óg", + "5465": "elő", + "5466": "lja", + "5467": "inta", + "5468": "ονται", + "5469": " gibt", + "5470": " sitä", + "5471": " fre", + "5472": "λύτε", + "5473": " sis", + "5474": " stvari", + "5475": "åt", + "5476": "ress", + "5477": "pper", + "5478": " Vai", + "5479": " spus", + "5480": " votre", + "5481": " kaikki", + "5482": "ню", + "5483": "šanu", + "5484": "šli", + "5485": " lugar", + "5486": " Cu", + "5487": "вропе", + "5488": " spor", + "5489": "ână", + "5490": " също", + "5491": "dev", + "5492": " ισ", + "5493": " būtu", + "5494": " così", + "5495": " pense", + "5496": " questa", + "5497": "ison", + "5498": "лага", + "5499": " rze", + "5500": " πό", + "5501": "ange", + "5502": "tima", + "5503": " vert", + "5504": "omis", + "5505": " πάν", + "5506": " cres", + "5507": "ště", + "5508": " ξε", + "5509": "zien", + "5510": " tyl", + "5511": " upor", + "5512": " indust", + "5513": "oh", + "5514": "nov", + "5515": "wij", + "5516": " были", + "5517": " kunne", + "5518": "лее", + "5519": " гла", + "5520": "wel", + "5521": "цен", + "5522": " át", + "5523": "cijas", + "5524": "нес", + "5525": "ką", + "5526": "rait", + "5527": "рно", + "5528": " neces", + "5529": " ped", + "5530": " dann", + "5531": " mert", + "5532": " vida", + "5533": " situa", + "5534": " sempre", + "5535": " abbiamo", + "5536": "сле", + "5537": "nies", + "5538": " być", + "5539": " jego", + "5540": " мені", + "5541": "aw", + "5542": " izm", + "5543": " gel", + "5544": "ņu", + "5545": "cir", + "5546": " Mal", + "5547": " ί", + "5548": "sid", + "5549": "чат", + "5550": " förs", + "5551": " absol", + "5552": " drugi", + "5553": " proti", + "5554": " Хо", + "5555": " багато", + "5556": "ča", + "5557": " ever", + "5558": " отри", + "5559": "tenant", + "5560": " hatte", + "5561": " hy", + "5562": " ihm", + "5563": " pia", + "5564": " всі", + "5565": " Jien", + "5566": "hos", + "5567": "atte", + "5568": " any", + "5569": " mhux", + "5570": "klich", + "5571": " eigen", + "5572": " Pu", + "5573": " vod", + "5574": " сві", + "5575": " trava", + "5576": "ują", + "5577": "inst", + "5578": " trabal", + "5579": "das", + "5580": " sia", + "5581": "way", + "5582": "лови", + "5583": "ování", + "5584": " last", + "5585": " бути", + "5586": "мента", + "5587": "itatea", + "5588": " persone", + "5589": "opa", + "5590": " Kon", + "5591": "enter", + "5592": "коно", + "5593": " leur", + "5594": " nech", + "5595": "fri", + "5596": " бя", + "5597": " цен", + "5598": " bħala", + "5599": " savez", + "5600": " really", + "5601": "ix", + "5602": " ál", + "5603": "yje", + "5604": "jich", + "5605": "θηκε", + "5606": " jei", + "5607": " тех", + "5608": " Komis", + "5609": "ερ", + "5610": "etr", + "5611": " même", + "5612": "otu", + "5613": "жду", + "5614": " лі", + "5615": " Nad", + "5616": " вклю", + "5617": " konkur", + "5618": "пла", + "5619": "чение", + "5620": "ír", + "5621": "orit", + "5622": " pessoas", + "5623": "θή", + "5624": "anz", + "5625": "til", + "5626": "umas", + "5627": " merc", + "5628": " stvar", + "5629": " повече", + "5630": "λου", + "5631": "вав", + "5632": " испо", + "5633": "νά", + "5634": "bet", + "5635": " said", + "5636": "ľa", + "5637": "ným", + "5638": "oku", + "5639": " χα", + "5640": " sü", + "5641": "aron", + "5642": "ço", + "5643": " cul", + "5644": "umiem", + "5645": " chose", + "5646": " bylo", + "5647": "pid", + "5648": " té", + "5649": " biss", + "5650": " σκ", + "5651": " зо", + "5652": " imma", + "5653": " нещо", + "5654": " avait", + "5655": " предста", + "5656": "erson", + "5657": " stabil", + "5658": "dal", + "5659": "ative", + "5660": " uč", + "5661": " kesk", + "5662": "цю", + "5663": "üh", + "5664": " ért", + "5665": " cond", + "5666": " denk", + "5667": "lá", + "5668": " kis", + "5669": " swo", + "5670": " bolo", + "5671": " tead", + "5672": " moeten", + "5673": "ór", + "5674": "ppe", + "5675": " anti", + "5676": "jut", + "5677": " ее", + "5678": "σσότε", + "5679": " Geor", + "5680": " είχε", + "5681": " então", + "5682": " περισσότε", + "5683": "uli", + "5684": " Las", + "5685": " Nor", + "5686": "gende", + "5687": " spos", + "5688": "wk", + "5689": "fach", + "5690": "vero", + "5691": "nyt", + "5692": " tou", + "5693": " būtų", + "5694": "ća", + "5695": "umā", + "5696": "ált", + "5697": "inda", + "5698": " hele", + "5699": " Iz", + "5700": " защото", + "5701": " partner", + "5702": "yllä", + "5703": " dez", + "5704": " nuo", + "5705": " wür", + "5706": " sigur", + "5707": " Če", + "5708": "icht", + "5709": "писа", + "5710": " jist", + "5711": "ťa", + "5712": "ρια", + "5713": "лия", + "5714": " töö", + "5715": " asem", + "5716": " echt", + "5717": " exemp", + "5718": "inud", + "5719": "ravo", + "5720": " gla", + "5721": " vég", + "5722": "ned", + "5723": "amil", + "5724": " może", + "5725": " були", + "5726": " these", + "5727": "ďal", + "5728": "ieważ", + "5729": " base", + "5730": " fare", + "5731": " multe", + "5732": "гли", + "5733": " две", + "5734": " poss", + "5735": "rez", + "5736": "ших", + "5737": " Dav", + "5738": " kör", + "5739": " peu", + "5740": "nika", + "5741": " Iva", + "5742": " run", + "5743": " prime", + "5744": " vienkārši", + "5745": "ļa", + "5746": " Ä", + "5747": "aig", + "5748": " lé", + "5749": "ager", + "5750": "erste", + "5751": " había", + "5752": "cos", + "5753": " κάπο", + "5754": "šte", + "5755": "реди", + "5756": " hot", + "5757": "ступ", + "5758": " dia", + "5759": " fem", + "5760": "govor", + "5761": "zej", + "5762": "нето", + "5763": "icu", + "5764": "oth", + "5765": "keit", + "5766": " tis", + "5767": " San", + "5768": "iems", + "5769": " mam", + "5770": "anten", + "5771": " perm", + "5772": " intern", + "5773": "ugh", + "5774": "mlu", + "5775": "osten", + "5776": " zien", + "5777": "urd", + "5778": "enza", + "5779": "десет", + "5780": " goed", + "5781": " mand", + "5782": " vier", + "5783": " aktiv", + "5784": " время", + "5785": " vē", + "5786": " ха", + "5787": " iespēj", + "5788": " κοι", + "5789": " κάτι", + "5790": "far", + "5791": "від", + "5792": "вата", + "5793": " gaan", + "5794": " tämä", + "5795": "aki", + "5796": " év", + "5797": "plat", + "5798": "tica", + "5799": " tikr", + "5800": " ασ", + "5801": "était", + "5802": " tutto", + "5803": " invest", + "5804": "row", + "5805": "вропей", + "5806": " tento", + "5807": "th", + "5808": "egy", + "5809": "чина", + "5810": " led", + "5811": " pēc", + "5812": " något", + "5813": " usk", + "5814": "ħab", + "5815": "λευ", + "5816": " ting", + "5817": " toda", + "5818": " έχουν", + "5819": "μφ", + "5820": " jį", + "5821": " učin", + "5822": " zelo", + "5823": " потрі", + "5824": " problém", + "5825": " Ag", + "5826": " Aust", + "5827": " Έτσι", + "5828": "umo", + "5829": " krit", + "5830": " hol", + "5831": " This", + "5832": " však", + "5833": "alifor", + "5834": " ütles", + "5835": "āv", + "5836": "ству", + "5837": " tab", + "5838": " Wenn", + "5839": "мати", + "5840": " чем", + "5841": "ograf", + "5842": "πή", + "5843": " ceea", + "5844": " mont", + "5845": " întâ", + "5846": " знаю", + "5847": " Sk", + "5848": " äh", + "5849": " projekt", + "5850": "вим", + "5851": " demok", + "5852": " other", + "5853": " Myslím", + "5854": "äll", + "5855": " tru", + "5856": " виде", + "5857": " ukoll", + "5858": "ational", + "5859": "íz", + "5860": "ént", + "5861": "дом", + "5862": "olat", + "5863": "ittle", + "5864": " Allura", + "5865": " wirklich", + "5866": "bolj", + "5867": " otro", + "5868": " tylko", + "5869": "ξε", + "5870": "kou", + "5871": "pel", + "5872": "inis", + "5873": " oike", + "5874": "aucoup", + "5875": " place", + "5876": "üs", + "5877": "ун", + "5878": "ħda", + "5879": " zum", + "5880": " jaun", + "5881": "áte", + "5882": "itti", + "5883": " bos", + "5884": "imos", + "5885": "лкова", + "5886": " rész", + "5887": "icz", + "5888": "skat", + "5889": " termin", + "5890": "țe", + "5891": "ħna", + "5892": " hay", + "5893": " hall", + "5894": "δι", + "5895": "iais", + "5896": " συν", + "5897": "ówi", + "5898": "anno", + "5899": " этом", + "5900": " ollut", + "5901": "udo", + "5902": "unto", + "5903": "клад", + "5904": "óż", + "5905": "ęd", + "5906": "ρίζ", + "5907": "ināt", + "5908": "sem", + "5909": "нні", + "5910": " Sc", + "5911": " sad", + "5912": " pur", + "5913": "rå", + "5914": "ползва", + "5915": " Wo", + "5916": "tö", + "5917": " Din", + "5918": " imm", + "5919": " vyt", + "5920": " мога", + "5921": "log", + "5922": " въз", + "5923": " уве", + "5924": "inder", + "5925": " prad", + "5926": " αρ", + "5927": "enda", + "5928": " acum", + "5929": " свои", + "5930": " obser", + "5931": " naħseb", + "5932": "ън", + "5933": " dawn", + "5934": " viss", + "5935": "ležit", + "5936": "ief", + "5937": "ход", + "5938": "ющи", + "5939": "áj", + "5940": " Mer", + "5941": " usp", + "5942": "denti", + "5943": " aqui", + "5944": " bych", + "5945": " більше", + "5946": "unud", + "5947": "ún", + "5948": "nar", + "5949": "рес", + "5950": " dů", + "5951": " κό", + "5952": " fos", + "5953": " padar", + "5954": "ίζω", + "5955": " lí", + "5956": "anju", + "5957": " einfach", + "5958": "ios", + "5959": "rii", + "5960": "owi", + "5961": "iger", + "5962": "ával", + "5963": "ούμε", + "5964": " lat", + "5965": "ću", + "5966": "inote", + "5967": " materi", + "5968": " Pal", + "5969": " fie", + "5970": " pic", + "5971": "ciu", + "5972": "tos", + "5973": " når", + "5974": " byla", + "5975": " nast", + "5976": " jų", + "5977": " Tor", + "5978": "heden", + "5979": " poder", + "5980": " because", + "5981": " который", + "5982": "āja", + "5983": "ční", + "5984": " important", + "5985": "ekom", + "5986": " Ser", + "5987": "λί", + "5988": " Χ", + "5989": " För", + "5990": " mj", + "5991": "nici", + "5992": "вания", + "5993": " вла", + "5994": " Lond", + "5995": " wsp", + "5996": "зь", + "5997": " sens", + "5998": " način", + "5999": "ρυ", + "6000": "ються", + "6001": " estão", + "6002": " potom", + "6003": "đu", + "6004": "zés", + "6005": "лич", + "6006": " hund", + "6007": "üt", + "6008": "ший", + "6009": " gł", + "6010": " спра", + "6011": "iekš", + "6012": " honom", + "6013": "ationen", + "6014": "нов", + "6015": "ρετε", + "6016": "omo", + "6017": "zor", + "6018": " blo", + "6019": " rok", + "6020": " още", + "6021": " rela", + "6022": " μεγά", + "6023": "ēd", + "6024": " rak", + "6025": " διά", + "6026": " μόνο", + "6027": "ков", + "6028": " Igen", + "6029": " norm", + "6030": "unca", + "6031": " dor", + "6032": " Mens", + "6033": "ült", + "6034": "ėtų", + "6035": " Jā", + "6036": " Dus", + "6037": " jim", + "6038": " пов", + "6039": "ará", + "6040": " sí", + "6041": " jät", + "6042": "ppo", + "6043": "ття", + "6044": " Gu", + "6045": " наи", + "6046": " clas", + "6047": " zato", + "6048": " tomto", + "6049": " web", + "6050": "aug", + "6051": "olt", + "6052": " liik", + "6053": "wy", + "6054": "ге", + "6055": "ною", + "6056": " rää", + "6057": " χρό", + "6058": " sina", + "6059": " kontrol", + "6060": "aram", + "6061": " jkun", + "6062": " gebru", + "6063": " толкова", + "6064": "ian", + "6065": "amise", + "6066": " două", + "6067": " võib", + "6068": "ικέ", + "6069": " ét", + "6070": " lor", + "6071": " ingen", + "6072": " nende", + "6073": "ški", + "6074": "amaan", + "6075": " orig", + "6076": " голо", + "6077": " tutti", + "6078": " может", + "6079": "dag", + "6080": " Θα", + "6081": "kken", + "6082": " poh", + "6083": "ontos", + "6084": "jší", + "6085": "ikti", + "6086": "мери", + "6087": " tuo", + "6088": " seus", + "6089": " λί", + "6090": " supp", + "6091": "кс", + "6092": "ilu", + "6093": " vro", + "6094": "wię", + "6095": " Ev", + "6096": " τόσο", + "6097": " преди", + "6098": " två", + "6099": " zwe", + "6100": " putea", + "6101": " probab", + "6102": "обре", + "6103": " mõt", + "6104": "ember", + "6105": " gøre", + "6106": " zdrav", + "6107": "κόμα", + "6108": " liel", + "6109": " ostat", + "6110": " toate", + "6111": " zp", + "6112": " hit", + "6113": " tras", + "6114": "bel", + "6115": " milli", + "6116": " beaucoup", + "6117": "ются", + "6118": "řed", + "6119": "iskt", + "6120": " noi", + "6121": " περί", + "6122": " jó", + "6123": " pré", + "6124": " Dies", + "6125": " contra", + "6126": " только", + "6127": "fo", + "6128": " long", + "6129": " tijd", + "6130": " divers", + "6131": " tým", + "6132": " χρη", + "6133": " għax", + "6134": " poate", + "6135": "gio", + "6136": "ovala", + "6137": " apenas", + "6138": " look", + "6139": " kuris", + "6140": " talál", + "6141": "owy", + "6142": "šta", + "6143": "лити", + "6144": " Για", + "6145": " ско", + "6146": " Mich", + "6147": " lill", + "6148": " wieħed", + "6149": ",.", + "6150": " ją", + "6151": " lö", + "6152": "unos", + "6153": " moj", + "6154": "ėra", + "6155": " stop", + "6156": "šķ", + "6157": " rés", + "6158": " praw", + "6159": "set", + "6160": "elet", + "6161": " mia", + "6162": " paz", + "6163": " immer", + "6164": "sl", + "6165": " vin", + "6166": " antes", + "6167": " could", + "6168": " συνέ", + "6169": "imy", + "6170": "orth", + "6171": "ложе", + "6172": " region", + "6173": "ése", + "6174": "alle", + "6175": "iaus", + "6176": "unci", + "6177": "ържа", + "6178": " mine", + "6179": "udu", + "6180": " sas", + "6181": "ordan", + "6182": " work", + "6183": " kohta", + "6184": " исполь", + "6185": "part", + "6186": "ások", + "6187": "чите", + "6188": " gewo", + "6189": " trimest", + "6190": " kän", + "6191": "ными", + "6192": " Sin", + "6193": " proc", + "6194": " financi", + "6195": " Gra", + "6196": " vom", + "6197": " могу", + "6198": " Β", + "6199": "jad", + "6200": "ksen", + "6201": " twee", + "6202": "łą", + "6203": "kiem", + "6204": "vern", + "6205": " Jer", + "6206": " jou", + "6207": " zi", + "6208": "oses", + "6209": "ēram", + "6210": " roh", + "6211": " vig", + "6212": " meine", + "6213": "uf", + "6214": "γε", + "6215": "onu", + "6216": " nj", + "6217": "eken", + "6218": "σιμο", + "6219": " Tako", + "6220": "utu", + "6221": " Ну", + "6222": " All", + "6223": " αυτά", + "6224": "ρει", + "6225": "áva", + "6226": " estamos", + "6227": "ibil", + "6228": " nici", + "6229": "øj", + "6230": "anu", + "6231": "ριο", + "6232": "ické", + "6233": "igur", + "6234": "mény", + "6235": " Gal", + "6236": " complet", + "6237": "ėk", + "6238": "ин", + "6239": " dl", + "6240": "utoč", + "6241": " jus", + "6242": " σχ", + "6243": "skih", + "6244": " σχε", + "6245": "tions", + "6246": "кан", + "6247": " chiar", + "6248": "бя", + "6249": "daj", + "6250": "pun", + "6251": "εία", + "6252": "erk", + "6253": " Åh", + "6254": " Mit", + "6255": " aga", + "6256": " cro", + "6257": "ameni", + "6258": " viac", + "6259": " fordi", + "6260": "nte", + "6261": "son", + "6262": "čne", + "6263": " Finn", + "6264": " vå", + "6265": "преде", + "6266": " sing", + "6267": " больше", + "6268": " how", + "6269": " tagħha", + "6270": "āka", + "6271": "вид", + "6272": " ор", + "6273": " enor", + "6274": " rece", + "6275": " неща", + "6276": "fl", + "6277": " minä", + "6278": " genom", + "6279": " velmi", + "6280": "řeb", + "6281": "рат", + "6282": "ците", + "6283": " еще", + "6284": " fund", + "6285": " ug", + "6286": "pect", + "6287": " Aga", + "6288": " sod", + "6289": " zgod", + "6290": " žmon", + "6291": "uth", + "6292": "nings", + "6293": " služ", + "6294": " iar", + "6295": " Parlament", + "6296": "θέ", + "6297": "ály", + "6298": "μέν", + "6299": " která", + "6300": "iens", + "6301": "mist", + "6302": "лись", + "6303": "ury", + "6304": "rati", + "6305": " fatto", + "6306": " sedan", + "6307": "čí", + "6308": "ough", + "6309": " esmu", + "6310": " какво", + "6311": " tiedätkö", + "6312": "iller", + "6313": " част", + "6314": "ical", + "6315": " konf", + "6316": " quanto", + "6317": "ated", + "6318": " Tre", + "6319": "этому", + "6320": " druh", + "6321": " παρά", + "6322": "нию", + "6323": " ше", + "6324": " ihre", + "6325": "olj", + "6326": " înc", + "6327": " jaoks", + "6328": " πράγμα", + "6329": " bev", + "6330": " zan", + "6331": " bere", + "6332": " right", + "6333": " ezt", + "6334": " tika", + "6335": " dobre", + "6336": " tus", + "6337": " čia", + "6338": "mt", + "6339": "žno", + "6340": " lot", + "6341": " nám", + "6342": " ener", + "6343": " przed", + "6344": "kum", + "6345": " sé", + "6346": " profes", + "6347": "elem", + "6348": " indiv", + "6349": "ßen", + "6350": "яви", + "6351": " Għal", + "6352": " paar", + "6353": " piem", + "6354": "isas", + "6355": " kah", + "6356": " osa", + "6357": " znate", + "6358": " Iżda", + "6359": " trochu", + "6360": " Uni", + "6361": " jista", + "6362": " ktorá", + "6363": " procent", + "6364": "ähr", + "6365": "znál", + "6366": " fue", + "6367": "ството", + "6368": " követ", + "6369": "ність", + "6370": " резу", + "6371": " public", + "6372": "raš", + "6373": " Pat", + "6374": " Ναι", + "6375": " няма", + "6376": "ům", + "6377": "enty", + "6378": "стру", + "6379": " cada", + "6380": "geb", + "6381": "qas", + "6382": "nica", + "6383": " Dat", + "6384": "iselt", + "6385": "arm", + "6386": " πά", + "6387": "inger", + "6388": "χι", + "6389": "čné", + "6390": " mest", + "6391": " dieser", + "6392": "yer", + "6393": " fö", + "6394": " нужно", + "6395": "vaj", + "6396": "нан", + "6397": "asse", + "6398": "stat", + "6399": "лися", + "6400": "мата", + "6401": "entar", + "6402": " kn", + "6403": " Cre", + "6404": " uporab", + "6405": "oren", + "6406": " παι", + "6407": " było", + "6408": " activ", + "6409": "kod", + "6410": " ky", + "6411": "лата", + "6412": "lichen", + "6413": "πι", + "6414": " συνε", + "6415": "igo", + "6416": "tava", + "6417": "érie", + "6418": " міс", + "6419": " dizer", + "6420": " midagi", + "6421": "wać", + "6422": " ця", + "6423": " mille", + "6424": "των", + "6425": "inės", + "6426": " zei", + "6427": "знача", + "6428": "nje", + "6429": "īga", + "6430": " Kol", + "6431": " rav", + "6432": " tip", + "6433": " pozn", + "6434": " jeśli", + "6435": " nadal", + "6436": " bef", + "6437": " fis", + "6438": " kunt", + "6439": "kam", + "6440": " dam", + "6441": "ortun", + "6442": " можу", + "6443": "čil", + "6444": " hus", + "6445": " teis", + "6446": "ateľ", + "6447": " Fre", + "6448": " cop", + "6449": " сти", + "6450": " commen", + "6451": " online", + "6452": " tant", + "6453": "mus", + "6454": "vät", + "6455": "жливо", + "6456": " glas", + "6457": " suure", + "6458": " Sí", + "6459": " aici", + "6460": " kasut", + "6461": "kri", + "6462": " Toda", + "6463": " jste", + "6464": "ņēm", + "6465": "ším", + "6466": "jest", + "6467": " uni", + "6468": " tous", + "6469": " μά", + "6470": "anne", + "6471": " Pos", + "6472": " Teraz", + "6473": "ső", + "6474": " Sm", + "6475": " stav", + "6476": "pal", + "6477": "вал", + "6478": " κυ", + "6479": " ки", + "6480": " Cor", + "6481": " sex", + "6482": " spole", + "6483": " produkt", + "6484": " gewoon", + "6485": " social", + "6486": "pär", + "6487": "ére", + "6488": " sf", + "6489": " lju", + "6490": " põh", + "6491": " rid", + "6492": " može", + "6493": " tilb", + "6494": "ements", + "6495": " Αν", + "6496": "ogle", + "6497": "vous", + "6498": " maks", + "6499": " system", + "6500": "under", + "6501": " aquí", + "6502": " jaar", + "6503": " sign", + "6504": " empres", + "6505": "ának", + "6506": " ανά", + "6507": " back", + "6508": " была", + "6509": "lex", + "6510": " fü", + "6511": " etwas", + "6512": " май", + "6513": " Face", + "6514": " YouT", + "6515": " fejn", + "6516": "ektr", + "6517": "aix", + "6518": "unde", + "6519": " šeit", + "6520": " arbej", + "6521": " jav", + "6522": "zioni", + "6523": " систе", + "6524": " използва", + "6525": "ye", + "6526": "вят", + "6527": " mé", + "6528": " yh", + "6529": "adores", + "6530": " diver", + "6531": " menos", + "6532": " kuidas", + "6533": "hez", + "6534": "čit", + "6535": "ální", + "6536": "ваме", + "6537": "tar", + "6538": "чин", + "6539": "fte", + "6540": "цій", + "6541": " úl", + "6542": " idő", + "6543": " mange", + "6544": "elig", + "6545": "мага", + "6546": " bola", + "6547": " пови", + "6548": "bles", + "6549": " mnog", + "6550": " heidän", + "6551": "wią", + "6552": " Kr", + "6553": " bodo", + "6554": "ege", + "6555": "plu", + "6556": "вий", + "6557": "телно", + "6558": "imu", + "6559": "unn", + "6560": "tir", + "6561": "ντα", + "6562": "ător", + "6563": " εκεί", + "6564": " velik", + "6565": " būs", + "6566": " Unter", + "6567": " autor", + "6568": " identi", + "6569": " sitten", + "6570": "дже", + "6571": "чко", + "6572": " Wh", + "6573": " atât", + "6574": " sagde", + "6575": "ång", + "6576": " Eh", + "6577": "drig", + "6578": " mám", + "6579": " zoals", + "6580": " diffic", + "6581": "kien", + "6582": " fiz", + "6583": "kelig", + "6584": " vrlo", + "6585": "аа", + "6586": " wid", + "6587": " üle", + "6588": " викори", + "6589": "γου", + "6590": " Czy", + "6591": " cho", + "6592": " fri", + "6593": " тук", + "6594": " pokud", + "6595": " até", + "6596": " jiġ", + "6597": " pid", + "6598": " более", + "6599": "ματο", + "6600": "endre", + "6601": " servi", + "6602": "ātad", + "6603": " πρώ", + "6604": "rz", + "6605": "õt", + "6606": " sw", + "6607": " mur", + "6608": " σαν", + "6609": " rapp", + "6610": " făcut", + "6611": "arijiet", + "6612": " ū", + "6613": "cel", + "6614": "dru", + "6615": "erta", + "6616": " zeg", + "6617": " issa", + "6618": " pasa", + "6619": " живо", + "6620": " Inter", + "6621": "EN", + "6622": "agu", + "6623": "χει", + "6624": " uw", + "6625": "anut", + "6626": "ander", + "6627": " brit", + "6628": "endant", + "6629": " będzie", + "6630": "dě", + "6631": "нем", + "6632": " ис", + "6633": " була", + "6634": " quindi", + "6635": "rou", + "6636": "чна", + "6637": " itt", + "6638": "bt", + "6639": "lož", + "6640": " Okay", + "6641": " этого", + "6642": "och", + "6643": "ový", + "6644": " tax", + "6645": "стори", + "6646": " wara", + "6647": " бі", + "6648": "etty", + "6649": "ôsob", + "6650": " Can", + "6651": " dve", + "6652": " rep", + "6653": " яка", + "6654": " vaat", + "6655": " ponieważ", + "6656": " alb", + "6657": "tem", + "6658": " hlav", + "6659": " mitte", + "6660": "kre", + "6661": " Hi", + "6662": " UE", + "6663": " coś", + "6664": " jid", + "6665": " При", + "6666": " tehn", + "6667": " ćemo", + "6668": " послед", + "6669": " davvero", + "6670": " Є", + "6671": "ási", + "6672": " ла", + "6673": "ihin", + "6674": " oameni", + "6675": " produk", + "6676": "уу", + "6677": "kaa", + "6678": "пъл", + "6679": " By", + "6680": " допо", + "6681": " nared", + "6682": "ėte", + "6683": "AAAA", + "6684": " ceva", + "6685": " sehen", + "6686": " stále", + "6687": "kuš", + "6688": "eeee", + "6689": "коло", + "6690": " hin", + "6691": " qualc", + "6692": " temps", + "6693": " cha", + "6694": " ora", + "6695": "йти", + "6696": "чита", + "6697": " polí", + "6698": " quar", + "6699": "æg", + "6700": " Oui", + "6701": "rī", + "6702": "con", + "6703": " кри", + "6704": " малко", + "6705": "ége", + "6706": "ħaj", + "6707": "isem", + "6708": "ális", + "6709": " anal", + "6710": " peri", + "6711": " todas", + "6712": "cup", + "6713": " dik", + "6714": " love", + "6715": " нача", + "6716": "ždy", + "6717": "udzi", + "6718": " poco", + "6719": " toen", + "6720": " puede", + "6721": "âr", + "6722": "τεύ", + "6723": " Sta", + "6724": " ach", + "6725": "къ", + "6726": "jeti", + "6727": " Lab", + "6728": " víte", + "6729": " capac", + "6730": "orden", + "6731": " kraj", + "6732": " авто", + "6733": " maail", + "6734": " unser", + "6735": " differen", + "6736": " tý", + "6737": " Aus", + "6738": " työ", + "6739": " oħra", + "6740": " waħda", + "6741": "hin", + "6742": "iejs", + "6743": "óóóó", + "6744": "škai", + "6745": " Kad", + "6746": "форма", + "6747": "IT", + "6748": "jä", + "6749": "ney", + "6750": "inio", + "6751": " West", + "6752": "itter", + "6753": " Zeit", + "6754": " final", + "6755": " tagasi", + "6756": " sellest", + "6757": "enu", + "6758": "вих", + "6759": "itse", + "6760": "liet", + "6761": " свої", + "6762": " utiliz", + "6763": "indo", + "6764": " kaks", + "6765": " daugiau", + "6766": "tia", + "6767": "род", + "6768": "rawdę", + "6769": " bend", + "6770": " diss", + "6771": "sä", + "6772": "itev", + "6773": "ħħar", + "6774": " art", + "6775": " ува", + "6776": " mnie", + "6777": " estar", + "6778": "stå", + "6779": "ádz", + "6780": " Дж", + "6781": " dess", + "6782": " sport", + "6783": " Tar", + "6784": "μμ", + "6785": "inas", + "6786": " Tātad", + "6787": "ρκ", + "6788": "tive", + "6789": " сле", + "6790": " torn", + "6791": " omdat", + "6792": "¡¡", + "6793": "istu", + "6794": " arg", + "6795": " folk", + "6796": " tiempo", + "6797": " Dit", + "6798": " Naj", + "6799": " Como", + "6800": " teha", + "6801": " tört", + "6802": " yhte", + "6803": "iesies", + "6804": " потому", + "6805": " opravdu", + "6806": "βά", + "6807": " Φ", + "6808": "ieš", + "6809": "ikk", + "6810": " kro", + "6811": " Добре", + "6812": "acă", + "6813": "кото", + "6814": " apoi", + "6815": " použ", + "6816": " wkoll", + "6817": " internet", + "6818": " наистина", + "6819": " où", + "6820": "vert", + "6821": "jí", + "6822": "йо", + "6823": "шь", + "6824": "bru", + "6825": "dej", + "6826": "τια", + "6827": " Ty", + "6828": " skr", + "6829": "φέ", + "6830": "liš", + "6831": " good", + "6832": "rs", + "6833": " Sì", + "6834": " mű", + "6835": "ról", + "6836": " ats", + "6837": "heder", + "6838": " Ra", + "6839": " zob", + "6840": "inga", + "6841": " тра", + "6842": "ewwel", + "6843": " where", + "6844": " както", + "6845": " necess", + "6846": " YouTube", + "6847": "вой", + "6848": " govor", + "6849": " tajjeb", + "6850": " лу", + "6851": " mud", + "6852": "вести", + "6853": " aussi", + "6854": " vissz", + "6855": "kyt", + "6856": "rel", + "6857": " áno", + "6858": " paci", + "6859": " κάνει", + "6860": "вого", + "6861": " jul", + "6862": " между", + "6863": "rā", + "6864": "pil", + "6865": "ască", + "6866": " godt", + "6867": " mlad", + "6868": " produc", + "6869": " Ber", + "6870": " škol", + "6871": " povedal", + "6872": "elm", + "6873": "tie", + "6874": " lar", + "6875": " має", + "6876": " bolj", + "6877": " niečo", + "6878": "AN", + "6879": "raž", + "6880": "wad", + "6881": " eb", + "6882": " make", + "6883": " trad", + "6884": " külön", + "6885": "fil", + "6886": "endes", + "6887": "accord", + "6888": " pul", + "6889": " Тепер", + "6890": "--", + "6891": "ús", + "6892": "adt", + "6893": "тре", + "6894": " insp", + "6895": " hovor", + "6896": "atro", + "6897": "лося", + "6898": " Rich", + "6899": " aldrig", + "6900": "ox", + "6901": " Ро", + "6902": "issä", + "6903": " vaš", + "6904": " ďal", + "6905": " пър", + "6906": "ването", + "6907": " politik", + "6908": " dois", + "6909": "eln", + "6910": "кту", + "6911": " cat", + "6912": " zah", + "6913": " тру", + "6914": " nada", + "6915": " két", + "6916": " qué", + "6917": "tac", + "6918": "ять", + "6919": "unta", + "6920": "úúúú", + "6921": "пера", + "6922": "роят", + "6923": " lejn", + "6924": " себе", + "6925": " point", + "6926": "wor", + "6927": " nõ", + "6928": " qal", + "6929": " sum", + "6930": " сре", + "6931": " Imma", + "6932": " nevar", + "6933": "aden", + "6934": " vedno", + "6935": " gol", + "6936": " još", + "6937": " μικ", + "6938": " jaut", + "6939": " mora", + "6940": " potre", + "6941": "ью", + "6942": "лом", + "6943": " Jūs", + "6944": "αααααααα", + "6945": " üh", + "6946": "liche", + "6947": " pien", + "6948": " Со", + "6949": "enea", + "6950": "μένη", + "6951": " ots", + "6952": " žin", + "6953": " histor", + "6954": " skutoč", + "6955": "ėd", + "6956": "nisk", + "6957": "rata", + "6958": "ките", + "6959": " Van", + "6960": " vän", + "6961": " besz", + "6962": " Са", + "6963": " нав", + "6964": "lement", + "6965": " agora", + "6966": "ΟΟ", + "6967": "dig", + "6968": "vie", + "6969": " fung", + "6970": " nors", + "6971": " toga", + "6972": " predstav", + "6973": "cri", + "6974": "kra", + "6975": " sagte", + "6976": " veste", + "6977": "щу", + "6978": "čan", + "6979": "сна", + "6980": " prijat", + "6981": "uren", + "6982": " list", + "6983": " fic", + "6984": " Finnish", + "6985": " tikrųjų", + "6986": "viz", + "6987": " сер", + "6988": " fast", + "6989": " posta", + "6990": " seis", + "6991": " många", + "6992": "лей", + "6993": " Sz", + "6994": " спе", + "6995": " labi", + "6996": " tehd", + "6997": " получи", + "6998": " bih", + "6999": "sioon", + "7000": " mucho", + "7001": " parlament", + "7002": "фі", + "7003": "жде", + "7004": " jä", + "7005": " civ", + "7006": " vic", + "7007": " aika", + "7008": " neid", + "7009": "desmit", + "7010": " seine", + "7011": " njegov", + "7012": " profess", + "7013": "зно", + "7014": " От", + "7015": "лове", + "7016": "jorde", + "7017": " Olen", + "7018": " fors", + "7019": "enz", + "7020": "σκε", + "7021": "спе", + "7022": " anc", + "7023": " olisi", + "7024": "ület", + "7025": " juh", + "7026": " lov", + "7027": " nue", + "7028": " až", + "7029": " ży", + "7030": "asid", + "7031": " ihn", + "7032": " imag", + "7033": " note", + "7034": " noti", + "7035": " eivät", + "7036": " jetzt", + "7037": "elikult", + "7038": "SSSS", + "7039": "вала", + "7040": " sla", + "7041": " slut", + "7042": " обе", + "7043": "manto", + "7044": " după", + "7045": " urmă", + "7046": " keine", + "7047": " ακόμα", + "7048": "bild", + "7049": "erte", + "7050": "itik", + "7051": " нико", + "7052": " little", + "7053": " virkelig", + "7054": " sì", + "7055": " pob", + "7056": " tím", + "7057": " тро", + "7058": "шение", + "7059": " indi", + "7060": " miejs", + "7061": "σια", + "7062": "isko", + "7063": " nal", + "7064": "aħħar", + "7065": " zdaj", + "7066": " gyven", + "7067": "тро", + "7068": " lø", + "7069": "ható", + "7070": " Een", + "7071": "στό", + "7072": " пар", + "7073": " μετά", + "7074": "stva", + "7075": "emaal", + "7076": "ových", + "7077": "qt", + "7078": "edy", + "7079": " ano", + "7080": " lop", + "7081": "oliko", + "7082": " tieto", + "7083": "äd", + "7084": "žas", + "7085": "тім", + "7086": "нете", + "7087": " hak", + "7088": " αρχ", + "7089": " szem", + "7090": " spolo", + "7091": " Facebo", + "7092": "ómo", + "7093": "znam", + "7094": " Nap", + "7095": "лення", + "7096": " Pres", + "7097": " nėra", + "7098": " than", + "7099": "ellement", + "7100": "bir", + "7101": "kke", + "7102": "pir", + "7103": "ulta", + "7104": " Wie", + "7105": " lon", + "7106": " tämän", + "7107": "аль", + "7108": "alne", + "7109": "timo", + "7110": "като", + "7111": " rah", + "7112": " uděl", + "7113": "ids", + "7114": "äch", + "7115": " andre", + "7116": " besch", + "7117": " nombre", + "7118": "ago", + "7119": "ível", + "7120": "μένα", + "7121": " орга", + "7122": " nogle", + "7123": " respons", + "7124": "ef", + "7125": "xim", + "7126": " оби", + "7127": " poch", + "7128": "ningar", + "7129": " pamat", + "7130": " pasak", + "7131": " mė", + "7132": " ens", + "7133": " mär", + "7134": " char", + "7135": " någon", + "7136": " tiene", + "7137": "pot", + "7138": "ffff", + "7139": "šana", + "7140": " ran", + "7141": "μαστε", + "7142": " tunn", + "7143": "taient", + "7144": " favor", + "7145": " različ", + "7146": " знаєте", + "7147": " зав", + "7148": " blij", + "7149": " sama", + "7150": " друго", + "7151": " alleen", + "7152": "ath", + "7153": "esn", + "7154": "pués", + "7155": " děl", + "7156": "lek", + "7157": "ško", + "7158": "дар", + "7159": "зни", + "7160": " yo", + "7161": " Ben", + "7162": "oms", + "7163": " něk", + "7164": " Brit", + "7165": " esam", + "7166": " lite", + "7167": " podr", + "7168": " pois", + "7169": " mesmo", + "7170": " todella", + "7171": "ieden", + "7172": " игра", + "7173": "esed", + "7174": " mali", + "7175": "ivad", + "7176": " skute", + "7177": "aid", + "7178": "āks", + "7179": " mysl", + "7180": " maken", + "7181": " прода", + "7182": "χαν", + "7183": " том", + "7184": "aires", + "7185": " fois", + "7186": " γιατί", + "7187": " rzeczy", + "7188": "jus", + "7189": "τών", + "7190": " Gesch", + "7191": " komun", + "7192": "erde", + "7193": " ici", + "7194": " nin", + "7195": " way", + "7196": " дея", + "7197": " lors", + "7198": " premi", + "7199": "лень", + "7200": " vasta", + "7201": "cen", + "7202": " ahora", + "7203": "omi", + "7204": "στά", + "7205": " dzī", + "7206": " say", + "7207": " daar", + "7208": " проду", + "7209": " unsere", + "7210": "bro", + "7211": "ħor", + "7212": "θρω", + "7213": "λεί", + "7214": " Го", + "7215": " rat", + "7216": " své", + "7217": " diesem", + "7218": " потрібно", + "7219": "κά", + "7220": "kao", + "7221": " εγ", + "7222": "vest", + "7223": " vou", + "7224": " control", + "7225": "ħu", + "7226": " dí", + "7227": " δι", + "7228": "iała", + "7229": " jelent", + "7230": " tikrai", + "7231": " Dž", + "7232": " puol", + "7233": " Europ", + "7234": " Kyllä", + "7235": " означа", + "7236": "ides", + "7237": " Mac", + "7238": " cou", + "7239": " mio", + "7240": " тем", + "7241": " свою", + "7242": " ν", + "7243": "éné", + "7244": " Kuid", + "7245": " first", + "7246": "διά", + "7247": " olid", + "7248": " vamos", + "7249": " sistem", + "7250": " bliver", + "7251": " asemenea", + "7252": "ajt", + "7253": "anos", + "7254": "ження", + "7255": " esse", + "7256": " pomo", + "7257": " telj", + "7258": " tudod", + "7259": "вительно", + "7260": "ното", + "7261": " apm", + "7262": "ienia", + "7263": " klaus", + "7264": " hö", + "7265": " viv", + "7266": "ból", + "7267": " λέ", + "7268": " rol", + "7269": " tah", + "7270": "глежда", + "7271": " några", + "7272": " europe", + "7273": "omm", + "7274": "rir", + "7275": " pala", + "7276": " Univers", + "7277": "ős", + "7278": "unut", + "7279": "лось", + "7280": " müs", + "7281": "чо", + "7282": "oki", + "7283": "eron", + "7284": "idel", + "7285": " ese", + "7286": " band", + "7287": " Nii", + "7288": " svi", + "7289": "aktar", + "7290": " správ", + "7291": " vastu", + "7292": "ssz", + "7293": " ps", + "7294": " están", + "7295": "σιμοποι", + "7296": " postav", + "7297": " ću", + "7298": "alta", + "7299": "cios", + "7300": " euh", + "7301": " продъл", + "7302": " puh", + "7303": "emmän", + "7304": "čnost", + "7305": " Port", + "7306": " '", + "7307": "sled", + "7308": " clar", + "7309": " Dakle", + "7310": " kapcs", + "7311": " elektr", + "7312": " origin", + "7313": "ês", + "7314": " Úgy", + "7315": " radi", + "7316": " вече", + "7317": " market", + "7318": "χο", + "7319": " Sir", + "7320": " обо", + "7321": " Willi", + "7322": " nista", + "7323": "amm", + "7324": "ulo", + "7325": "ńst", + "7326": "atel", + "7327": "ogen", + "7328": "viet", + "7329": " Sen", + "7330": " tiež", + "7331": " sistema", + "7332": "gev", + "7333": "iat", + "7334": "ėje", + "7335": " acho", + "7336": " инте", + "7337": " medic", + "7338": "οο", + "7339": "inde", + "7340": "зова", + "7341": " подо", + "7342": " volta", + "7343": "olg", + "7344": " lá", + "7345": " Tra", + "7346": "yg", + "7347": "eko", + "7348": "rio", + "7349": "ное", + "7350": "ffen", + "7351": "stan", + "7352": " halu", + "7353": "zon", + "7354": " jgħ", + "7355": " dollar", + "7356": "xxxx", + "7357": " kall", + "7358": " volna", + "7359": " oblasti", + "7360": "кон", + "7361": " well", + "7362": " projek", + "7363": " naprawdę", + "7364": "τεί", + "7365": "νεται", + "7366": " hran", + "7367": " trav", + "7368": " institu", + "7369": " Ва", + "7370": " czę", + "7371": " cose", + "7372": "eds", + "7373": " Stre", + "7374": "éz", + "7375": " sun", + "7376": " down", + "7377": " fak", + "7378": " sle", + "7379": " päiv", + "7380": " yrit", + "7381": " день", + "7382": "dob", + "7383": " add", + "7384": " fej", + "7385": " país", + "7386": " iż", + "7387": " Así", + "7388": "amais", + "7389": " esim", + "7390": " kuten", + "7391": "лни", + "7392": "ingu", + "7393": " Jap", + "7394": "пы", + "7395": "зви", + "7396": " наше", + "7397": " somet", + "7398": " tāpēc", + "7399": " gjorde", + "7400": "roš", + "7401": "čně", + "7402": " γε", + "7403": " νέ", + "7404": " typ", + "7405": " onde", + "7406": " одно", + "7407": " anden", + "7408": "сен", + "7409": "tiva", + "7410": " lad", + "7411": " части", + "7412": "ък", + "7413": " θε", + "7414": " cet", + "7415": " pela", + "7416": " Τα", + "7417": "ativa", + "7418": " další", + "7419": " nějak", + "7420": " повер", + "7421": "žd", + "7422": "ność", + "7423": " bár", + "7424": " rov", + "7425": " σου", + "7426": "ности", + "7427": " vari", + "7428": " anderen", + "7429": "née", + "7430": "atos", + "7431": "elta", + "7432": " rast", + "7433": " blive", + "7434": " gesch", + "7435": "ozaj", + "7436": " ton", + "7437": " chod", + "7438": " зали", + "7439": " Vis", + "7440": " impl", + "7441": " pärast", + "7442": " Menschen", + "7443": "ők", + "7444": "διο", + "7445": "endi", + "7446": " niż", + "7447": "ises", + "7448": "нови", + "7449": " dij", + "7450": " ros", + "7451": " robi", + "7452": " estaba", + "7453": " allt", + "7454": " suas", + "7455": "sew", + "7456": " Ci", + "7457": "ville", + "7458": " sada", + "7459": "ността", + "7460": " naozaj", + "7461": "нок", + "7462": "otto", + "7463": "žeme", + "7464": " Hon", + "7465": " kalb", + "7466": " pati", + "7467": " wiesz", + "7468": "кою", + "7469": " сп", + "7470": "ният", + "7471": " kie", + "7472": " sre", + "7473": "вайте", + "7474": " aast", + "7475": " varētu", + "7476": "fal", + "7477": "ieb", + "7478": " kér", + "7479": " inde", + "7480": "λογ", + "7481": " åt", + "7482": " Па", + "7483": "ário", + "7484": " jit", + "7485": "amine", + "7486": "ligen", + "7487": " arvan", + "7488": " maintenant", + "7489": "inę", + "7490": " ша", + "7491": " Sel", + "7492": " anim", + "7493": " igaz", + "7494": " määr", + "7495": " comer", + "7496": " opera", + "7497": "γω", + "7498": " mov", + "7499": " zach", + "7500": " posled", + "7501": " administ", + "7502": "ety", + "7503": " ieg", + "7504": " würde", + "7505": "ără", + "7506": " Vor", + "7507": "akter", + "7508": "publi", + "7509": " mundo", + "7510": " nunca", + "7511": " sł", + "7512": " благо", + "7513": "ūk", + "7514": "tri", + "7515": "seen", + "7516": " Fin", + "7517": "ħabba", + "7518": " sitt", + "7519": "wet", + "7520": " tarko", + "7521": "вете", + "7522": " kau", + "7523": " чове", + "7524": " resta", + "7525": " nastav", + "7526": " element", + "7527": "ual", + "7528": " хм", + "7529": "ėjau", + "7530": "льта", + "7531": " gov", + "7532": " näh", + "7533": " tada", + "7534": " člov", + "7535": "cev", + "7536": "cza", + "7537": "koj", + "7538": "čio", + "7539": "aliza", + "7540": " assim", + "7541": "єю", + "7542": "дра", + "7543": "лото", + "7544": " bran", + "7545": " nüüd", + "7546": "ninger", + "7547": "kę", + "7548": "лите", + "7549": " бли", + "7550": "andid", + "7551": " hyvä", + "7552": " máme", + "7553": " tilbage", + "7554": "rede", + "7555": " nič", + "7556": " Euró", + "7557": " kiek", + "7558": " jedno", + "7559": " God", + "7560": " maz", + "7561": " tog", + "7562": " vit", + "7563": " кли", + "7564": "iseen", + "7565": " arba", + "7566": "νια", + "7567": " Ti", + "7568": "bile", + "7569": " τρό", + "7570": "ennem", + "7571": " Zato", + "7572": " saat", + "7573": " Bl", + "7574": " aki", + "7575": " ier", + "7576": " nekoliko", + "7577": "ník", + "7578": "tivo", + "7579": "ками", + "7580": " Dur", + "7581": " iem", + "7582": "цията", + "7583": " tová", + "7584": " οικο", + "7585": " робо", + "7586": " хоче", + "7587": "yttä", + "7588": " Net", + "7589": " pelo", + "7590": " minist", + "7591": " Ή", + "7592": "τικέ", + "7593": " Star", + "7594": " lige", + "7595": " Jeśli", + "7596": "ect", + "7597": " rit", + "7598": " perf", + "7599": "uru", + "7600": "đer", + "7601": "itus", + "7602": "ктив", + "7603": " That", + "7604": " prieš", + "7605": " wissen", + "7606": "apa", + "7607": "ogo", + "7608": " incl", + "7609": " avoir", + "7610": " mennes", + "7611": " mindig", + "7612": "hány", + "7613": " jut", + "7614": " sido", + "7615": " decir", + "7616": " kapit", + "7617": "šla", + "7618": " Ye", + "7619": " ori", + "7620": "tavat", + "7621": "zenia", + "7622": " Dru", + "7623": " поч", + "7624": "ěd", + "7625": " sir", + "7626": " iets", + "7627": "ság", + "7628": "шим", + "7629": "ikus", + "7630": "lege", + "7631": " sätt", + "7632": " cosas", + "7633": " coisas", + "7634": "daw", + "7635": "ješ", + "7636": "čaj", + "7637": "eben", + "7638": " weg", + "7639": " tend", + "7640": "ąd", + "7641": "πέ", + "7642": "zion", + "7643": " kép", + "7644": " naf", + "7645": " grad", + "7646": " mówi", + "7647": " pozi", + "7648": " certa", + "7649": " nieko", + "7650": " puțin", + "7651": " zí", + "7652": " trou", + "7653": " trebui", + "7654": "oru", + "7655": "ensä", + "7656": "jena", + "7657": " mama", + "7658": " ellen", + "7659": "üks", + "7660": " Εν", + "7661": "šimt", + "7662": "uksen", + "7663": " għad", + "7664": " saada", + "7665": " eftersom", + "7666": " personas", + "7667": "δί", + "7668": "šet", + "7669": "edes", + "7670": "lust", + "7671": " hang", + "7672": " signific", + "7673": " вз", + "7674": "erst", + "7675": " Sal", + "7676": "gte", + "7677": "inc", + "7678": " îl", + "7679": "sche", + "7680": "ůsob", + "7681": "pportun", + "7682": " podpor", + "7683": " può", + "7684": "ystem", + "7685": "änder", + "7686": " izmanto", + "7687": "ζί", + "7688": "asa", + "7689": "ote", + "7690": " мя", + "7691": "rukt", + "7692": " tehdä", + "7693": " zrobi", + "7694": " či", + "7695": " aps", + "7696": "idente", + "7697": " const", + "7698": " rohkem", + "7699": " χρησιμοποι", + "7700": "от", + "7701": "ква", + "7702": "τηση", + "7703": " Mil", + "7704": " кла", + "7705": "kollu", + "7706": " tych", + "7707": " desde", + "7708": " ktorí", + "7709": "šel", + "7710": " næ", + "7711": "ijek", + "7712": " esp", + "7713": " спа", + "7714": " чув", + "7715": " leid", + "7716": " svoju", + "7717": "umb", + "7718": " Де", + "7719": " ос", + "7720": "ches", + "7721": "eles", + "7722": "ложи", + "7723": " edu", + "7724": " sko", + "7725": " dove", + "7726": "κή", + "7727": "emy", + "7728": "imin", + "7729": "iski", + "7730": "unden", + "7731": " Para", + "7732": " noss", + "7733": " teie", + "7734": " dokon", + "7735": " europ", + "7736": "turi", + "7737": " zac", + "7738": " εργ", + "7739": "enten", + "7740": " veik", + "7741": " våra", + "7742": "zw", + "7743": "ža", + "7744": "osť", + "7745": "aniu", + "7746": " När", + "7747": "eller", + "7748": " spun", + "7749": " hyvin", + "7750": " ihmis", + "7751": " истори", + "7752": "nil", + "7753": " žen", + "7754": " ming", + "7755": "χω", + "7756": " tá", + "7757": "etek", + "7758": "нова", + "7759": " pă", + "7760": "tiin", + "7761": " bomo", + "7762": " cerc", + "7763": " καθώ", + "7764": " ancora", + "7765": " έχουμε", + "7766": "кли", + "7767": "aval", + "7768": " noz", + "7769": "allen", + "7770": " gled", + "7771": " magħ", + "7772": " этот", + "7773": "js", + "7774": "тни", + "7775": " cie", + "7776": " práv", + "7777": " mejor", + "7778": " posit", + "7779": " prest", + "7780": " уча", + "7781": " veľa", + "7782": " sulla", + "7783": "imų", + "7784": "akin", + "7785": " App", + "7786": " смо", + "7787": " olnud", + "7788": " există", + "7789": " realiz", + "7790": "jos", + "7791": "λοι", + "7792": " бъ", + "7793": "tség", + "7794": "мите", + "7795": " pár", + "7796": " tudo", + "7797": " nikdy", + "7798": " spôsob", + "7799": "dien", + "7800": " Kdy", + "7801": " aller", + "7802": "ствительно", + "7803": "ňa", + "7804": "kul", + "7805": "ély", + "7806": "čka", + "7807": " anos", + "7808": "etu", + "7809": "acji", + "7810": "onių", + "7811": " mij", + "7812": "тем", + "7813": "ness", + "7814": " Ils", + "7815": " Jie", + "7816": " gem", + "7817": " zelf", + "7818": " явля", + "7819": " valst", + "7820": "nat", + "7821": " Op", + "7822": " već", + "7823": " фор", + "7824": " Bill", + "7825": " nakon", + "7826": " υπάρχει", + "7827": "omu", + "7828": " äl", + "7829": "iego", + "7830": " Che", + "7831": " gut", + "7832": " kész", + "7833": " проце", + "7834": "ņa", + "7835": " Ще", + "7836": "jort", + "7837": "ljiv", + "7838": " cij", + "7839": " weil", + "7840": " müssen", + "7841": "ць", + "7842": "gue", + "7843": " tuk", + "7844": " vár", + "7845": " parl", + "7846": " τώρα", + "7847": " хочу", + "7848": "ślę", + "7849": "μού", + "7850": " only", + "7851": " őket", + "7852": "alment", + "7853": " model", + "7854": " todėl", + "7855": " használ", + "7856": "ettu", + "7857": " pew", + "7858": " tež", + "7859": " keres", + "7860": " pogled", + "7861": " програ", + "7862": " telefon", + "7863": " ", + "7864": "e", + "7865": "a", + "7866": "i", + "7867": "n", + "7868": "t", + "7869": "o", + "7870": "s", + "7871": "r", + "7872": "l", + "7873": "u", + "7874": "d", + "7875": "m", + "7876": "k", + "7877": ",", + "7878": "p", + "7879": "v", + "7880": "c", + "7881": "g", + "7882": "h", + "7883": ".", + "7884": "j", + "7885": "о", + "7886": "b", + "7887": "а", + "7888": "z", + "7889": "е", + "7890": "и", + "7891": "т", + "7892": "н", + "7893": "y", + "7894": "f", + "7895": "в", + "7896": "р", + "7897": "с", + "7898": "w", + "7899": "α", + "7900": "л", + "7901": "к", + "7902": "д", + "7903": "ä", + "7904": "м", + "7905": "τ", + "7906": "é", + "7907": "ο", + "7908": "ε", + "7909": "п", + "7910": "á", + "7911": "ι", + "7912": "у", + "7913": "ν", + "7914": "я", + "7915": "з", + "7916": "š", + "7917": "і", + "7918": "-", + "7919": "q", + "7920": "í", + "7921": "б", + "7922": "σ", + "7923": "S", + "7924": "A", + "7925": "ρ", + "7926": "ž", + "7927": "г", + "7928": "π", + "7929": "ч", + "7930": "č", + "7931": "'", + "7932": "μ", + "7933": "ā", + "7934": "T", + "7935": "κ", + "7936": "E", + "7937": "M", + "7938": "υ", + "7939": "ă", + "7940": "ö", + "7941": "ь", + "7942": "D", + "7943": "ħ", + "7944": "η", + "7945": "I", + "7946": "P", + "7947": "N", + "7948": "ó", + "7949": "λ", + "7950": "x", + "7951": "ж", + "7952": "й", + "7953": "å", + "7954": "ί", + "7955": "ό", + "7956": "?", + "7957": "ά", + "7958": "х", + "7959": "J", + "7960": "C", + "7961": "ы", + "7962": "έ", + "7963": "B", + "7964": "H", + "7965": "ü", + "7966": "K", + "7967": "ш", + "7968": "L", + "7969": "V", + "7970": "O", + "7971": "ą", + "7972": "ē", + "7973": "ц", + "7974": "ż", + "7975": "ī", + "7976": "ю", + "7977": "γ", + "7978": "щ", + "7979": "ъ", + "7980": "ł", + "7981": "δ", + "7982": "ý", + "7983": "ę", + "7984": "ė", + "7985": "ě", + "7986": "ω", + "7987": "U", + "7988": "ή", + "7989": "ú", + "7990": "G", + "7991": "R", + "7992": "W", + "7993": "ș", + "7994": "χ", + "7995": "F", + "7996": "õ", + "7997": "ć", + "7998": "ț", + "7999": "θ", + "8000": "ų", + "8001": "î", + "8002": "ύ", + "8003": "ū", + "8004": "ř", + "8005": "ġ", + "8006": "ã", + "8007": "à", + "8008": "ś", + "8009": "ő", + "8010": "ť", + "8011": "æ", + "8012": "ώ", + "8013": "э", + "8014": "φ", + "8015": "ø", + "8016": "ņ", + "8017": "è", + "8018": "є", + "8019": "Z", + "8020": "!", + "8021": "į", + "8022": "ф", + "8023": "β", + "8024": "â", + "8025": "ç", + "8026": "Т", + "8027": "ê", + "8028": "ċ", + "8029": "Н", + "8030": "ї", + "8031": "В", + "8032": "ξ", + "8033": ":", + "8034": "С", + "8035": "ů", + "8036": "ζ", + "8037": "П", + "8038": "ľ", + "8039": "Д", + "8040": "Y", + "8041": "К", + "8042": "М", + "8043": "О", + "8044": "ļ", + "8045": "ô", + "8046": "И", + "8047": "Q", + "8048": "А", + "8049": "Я", + "8050": "Α", + "8051": "Б", + "8052": "Ε", + "8053": "ď", + "8054": "ß", + "8055": "З", + "8056": "Τ", + "8057": "Š", + "8058": "Κ", + "8059": "đ", + "8060": "Ο", + "8061": "ű", + "8062": "Е", + "8063": "ň", + "8064": "Σ", + "8065": "Π", + "8066": "ñ", + "8067": "ù", + "8068": "Μ", + "8069": "У", + "8070": "É", + "8071": "ψ", + "8072": "Р", + "8073": "ń", + "8074": "Г", + "8075": "ò", + "8076": "ì", + "8077": "Č", + "8078": "Ц", + "8079": "Л", + "8080": "¿", + "8081": "Э", + "8082": "Х", + "8083": "Ș", + "8084": "ķ", + "8085": "Η", + "8086": "Έ", + "8087": "Î", + "8088": "І", + "8089": "Ч", + "8090": "Ν", + "8091": "X", + "8092": "ģ", + "8093": "Δ", + "8094": "Γ", + "8095": "Ž", + "8096": "Ф", + "8097": "ź", + "8098": "¡", + "8099": "Á", + "8100": "È", + "8101": "ë", + "8102": "Θ", + "8103": "Ό", + "8104": "Ω", + "8105": "Щ", + "8106": "Ú", + "8107": "Ш", + "8108": "Λ", + "8109": "Ü", + "8110": "Å", + "8111": "Ä", + "8112": "Ι", + "8113": "û", + "8114": "ϊ", + "8115": "Χ", + "8116": "Β", + "8117": "Є", + "8118": "Φ", + "8119": "Ρ", + "8120": "Й", + "8121": "Ħ", + "8122": "Ġ", + "8123": "Ή", + "8124": "Ó", + "8125": "Ö", + "8126": "Υ", + "8127": "Í", + "8128": "Ά", + "8129": "ï", + "8130": "œ", + "8131": "À", + "8132": "%", + "8133": "Ж", + "8134": "Ξ", + "8135": "Ю", + "8136": "Ř", + "8137": "Ő", + "8138": "Ď", + "8139": "Ї", + "8140": "Ċ", + "8141": "ŕ", + "8142": "Į", + "8143": "Ż", + "8144": "ĺ", + "8145": "Ś", + "8146": "Ā", + "8147": "/", + "8148": "Ņ", + "8149": "Ζ", + "8150": "ґ", + "8151": "Ç", + "8152": "Õ", + "8153": "Ķ", + "8154": "Ī", + "8155": "Ľ", + "8156": "Ļ", + "8157": "Ē", + "8158": "Ț", + "8159": "Ł", + "8160": "Æ", + "8161": "Ø", + "8162": "Ê", + "8163": "Ь", + "8164": "Ă", + "8165": "Ъ", + "8166": "Ū", + "8167": "Ψ", + "8168": "Ģ", + "8169": "Ė", + "8170": "Ы", + "8171": "Ć", + "8172": "Ě", + "8173": "Ę", + "8174": "Ą", + "8175": "Ť", + "8176": "Ý", + "8177": "Ґ", + "8178": "Ô", + "8179": "$", + "8180": "Â", + "8181": "Ã", + "8182": "Ų", + "8183": "Ű", + "8184": "Ź", + "8185": "€", + "8186": "Ů", + "8187": "Ň", + "8188": "£", + "8189": "Ì", + "8190": "Ń", + "8191": "Œ" +} \ No newline at end of file diff --git a/bark-3.0/Decoder.mlmodelc/analytics/coremldata.bin b/bark-3.0/Decoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..fb631b26c861bff7c243e6ea12e23f2e5afc39f7 --- /dev/null +++ b/bark-3.0/Decoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6845726bb7bb79b418c1e0019af13eca816ad7a6e3bd8eace241ae63197712c2 +size 243 diff --git a/bark-3.0/Decoder.mlmodelc/coremldata.bin b/bark-3.0/Decoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..810f3270882ad842120000165609f8557e3ff478 --- /dev/null +++ b/bark-3.0/Decoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:28a03db82d95bc2c5a48a6c446942ec43452b45d505a77485e5e02b55c3c46dc +size 563 diff --git a/bark-3.0/Decoder.mlmodelc/metadata.json b/bark-3.0/Decoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1a47585c4f70466a4f5b50b5e8b552be89a90e4b --- /dev/null +++ b/bark-3.0/Decoder.mlmodelc/metadata.json @@ -0,0 +1,123 @@ +[ + { + "metadataOutputVersion" : "3.0", + "shortDescription" : "Underdog Bark 3.0 decoder (RNNT prediction network)", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 640 × 1)", + "shortDescription" : "", + "shape" : "[1, 640, 1]", + "name" : "decoder", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "h_out", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "c_out", + "type" : "MultiArray" + } + ], + "storagePrecision" : "Float16", + "modelParameters" : [ + + ], + "author" : "Fluid Inference", + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Select" : 1, + "Ios17.squeeze" : 4, + "Ios17.gather" : 1, + "Ios17.cast" : 8, + "Ios17.lstm" : 2, + "Split" : 2, + "Ios17.add" : 1, + "Ios17.transpose" : 2, + "Ios17.greaterEqual" : 1, + "Identity" : 1, + "Stack" : 2 + }, + "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1]", + "name" : "targets", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "target_length", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "h_in", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 1 × 640)", + "shortDescription" : "", + "shape" : "[2, 1, 640]", + "name" : "c_in", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-10-21", + "com.github.apple.coremltools.source" : "torch==2.7.0", + "com.github.apple.coremltools.version" : "9.0b1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "Decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/bark-3.0/Decoder.mlmodelc/model.mil b/bark-3.0/Decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..1177d26b92ef8aa5b442604ee7ba03553ce1ca96 --- /dev/null +++ b/bark-3.0/Decoder.mlmodelc/model.mil @@ -0,0 +1,73 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] +{ + func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { + tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; + tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; + tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; + tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; + tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_9")]; + tensor cast_1 = cast(dtype = cast_1_dtype_0, x = targets_to_int16)[name = tensor("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; + tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor(8193)]; + tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = tensor("add_2")]; + tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = tensor("select_0")]; + tensor y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor("y_cast_fp16_cast_uint16_axis_0"), val = tensor(0)]; + tensor select_0_to_int16_dtype_0 = const()[name = tensor("select_0_to_int16_dtype_0"), val = tensor("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor("cast_7")]; + tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; + tensor split_0_num_splits_0 = const()[name = tensor("split_0_num_splits_0"), val = tensor(2)]; + tensor split_0_axis_0 = const()[name = tensor("split_0_axis_0"), val = tensor(0)]; + tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_6")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = tensor("split_0_cast_fp16")]; + tensor split_1_num_splits_0 = const()[name = tensor("split_1_num_splits_0"), val = tensor(2)]; + tensor split_1_axis_0 = const()[name = tensor("split_1_axis_0"), val = tensor(0)]; + tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_5")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = tensor("split_1_cast_fp16")]; + tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_direction_0 = const()[name = tensor("input_lstm_layer_0_direction_0"), val = tensor("forward")]; + tensor input_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_lstm_layer_0_activation_0 = const()[name = tensor("input_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10487168)))]; + tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13764032)))]; + tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17040896)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor("transpose_2")]; + tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_lstm_layer_0_cast_fp16")]; + tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; + tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; + tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; + tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; + tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; + tensor concat_4_to_fp16 = const()[name = tensor("concat_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17046080)))]; + tensor concat_5_to_fp16 = const()[name = tensor("concat_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20322944)))]; + tensor concat_3_to_fp16 = const()[name = tensor("concat_3_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23599808)))]; + tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = tensor("input_cast_fp16")]; + tensor obj_3_axis_0 = const()[name = tensor("obj_3_axis_0"), val = tensor(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = tensor("obj_3_cast_fp16")]; + tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor obj_axis_0 = const()[name = tensor("obj_axis_0"), val = tensor(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = tensor("obj_cast_fp16")]; + tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; + tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; + tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_2")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_3")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_4")]; + tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; + } -> (decoder, h_out, c_out); +} \ No newline at end of file diff --git a/bark-3.0/Decoder.mlmodelc/weights/weight.bin b/bark-3.0/Decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3138808bc7ad8e708e9230458ce393e9da7332b4 --- /dev/null +++ b/bark-3.0/Decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2331b87b4f9db9f0eff71eae05df427a19d47402882d52ca8eadf1c856040cf +size 23604992 diff --git a/bark-3.0/Encoder.mlmodelc/analytics/coremldata.bin b/bark-3.0/Encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..bd97358570e4c406725a7c1e5b483a8ada9b4d6c --- /dev/null +++ b/bark-3.0/Encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03ea5a53546d8685994980f95dba47b2b5d6e80c847515a8b1e8231ab975d233 +size 243 diff --git a/bark-3.0/Encoder.mlmodelc/coremldata.bin b/bark-3.0/Encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5703c268b48840d2bd54bde73f8f85c13487801b --- /dev/null +++ b/bark-3.0/Encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b84bc0a623f0615306c6419592e0702fa023f36783e16ebd77f67b9fc5d9dd0a +size 557 diff --git a/bark-3.0/Encoder.mlmodelc/model.mil b/bark-3.0/Encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..dc79694dc76d2ca101e65d1e997527409b187b3b --- /dev/null +++ b/bark-3.0/Encoder.mlmodelc/model.mil @@ -0,0 +1,3644 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor mel, tensor mel_length) { + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + string var_86_to_fp16_dtype_0 = const()[name = string("op_86_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor mel_length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = mel_length)[name = string("cast_3")]; + tensor var_88_cast_fp16 = add(x = mel_length_to_fp16, y = var_87_promoted_to_fp16)[name = string("op_88_cast_fp16")]; + fp16 _inversed_90_y_0_to_fp16 = const()[name = string("_inversed_90_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = string("_inversed_90_cast_fp16")]; + fp16 var_91_to_fp16 = const()[name = string("op_91_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = string("lengths_1_cast_fp16")]; + tensor lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = string("lengths_3_cast_fp16")]; + fp16 var_95_promoted_to_fp16 = const()[name = string("op_95_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = string("op_96_cast_fp16")]; + fp16 _inversed_98_y_0_to_fp16 = const()[name = string("_inversed_98_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = string("_inversed_98_cast_fp16")]; + fp16 var_99_to_fp16 = const()[name = string("op_99_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = string("lengths_7_cast_fp16")]; + tensor lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = string("lengths_9_cast_fp16")]; + fp16 var_103_promoted_to_fp16 = const()[name = string("op_103_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = string("op_104_cast_fp16")]; + fp16 _inversed_106_y_0_to_fp16 = const()[name = string("_inversed_106_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = string("_inversed_106_cast_fp16")]; + fp16 var_107_to_fp16 = const()[name = string("op_107_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = string("lengths_13_cast_fp16")]; + tensor lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = string("lengths_cast_fp16")]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_2")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_315")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = string("input_1_cast_fp16")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([2, 2])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = string("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4736)))]; + tensor input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor([2, 2])]; + int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(256)]; + tensor input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = string("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5312)))]; + tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9984)))]; + tensor input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = string("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10560)))]; + tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141696)))]; + tensor input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")]; + tensor input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor([2, 2])]; + int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(256)]; + tensor input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = string("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142272)))]; + tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146944)))]; + tensor input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = string("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147520)))]; + tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278656)))]; + tensor input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_157_perm_0 = const()[name = string("op_157_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_158 = const()[name = string("op_158"), val = tensor([1, 188, -1])]; + tensor var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = string("transpose_314")]; + tensor input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor module_pre_encode_out_weight_to_fp16 = const()[name = string("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279232)))]; + tensor module_pre_encode_out_bias_to_fp16 = const()[name = string("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8667904)))]; + tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = string("linear_0_cast_fp16")]; + string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670016)))]; + tensor var_196_axes_0 = const()[name = string("op_196_axes_0"), val = tensor([-1])]; + tensor encoder_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = string("cast_1")]; + tensor var_196 = expand_dims(axes = var_196_axes_0, x = encoder_length)[name = string("op_196")]; + tensor pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = string("pad_mask_1")]; + tensor var_198_axes_0 = const()[name = string("op_198_axes_0"), val = tensor([1])]; + tensor var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = string("op_198")]; + tensor var_199 = const()[name = string("op_199"), val = tensor([1, 188, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = string("pad_mask_for_att_mask_1")]; + tensor var_201_perm_0 = const()[name = string("op_201_perm_0"), val = tensor([0, 2, 1])]; + tensor var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = string("pad_mask_for_att_mask")]; + tensor const_7 = const()[name = string("const_7"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = string("att_mask")]; + tensor mask_1 = logical_not(x = att_mask)[name = string("mask_1")]; + tensor pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")]; + tensor input_21_axes_0 = const()[name = string("input_21_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670848)))]; + tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8672960)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8675072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9199424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11309120))))[name = string("module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_1_bias_0_to_fp16 = const()[name = string("linear_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11374720)))]; + tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_21_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11382976))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11907328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14015168))))[name = string("module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14031616)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_25_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_232_to_fp16 = const()[name = string("op_232_to_fp16"), val = fp16(0x1p-1)]; + tensor var_233_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_232_to_fp16)[name = string("op_233_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_233_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14033728)))]; + tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14035840)))]; + tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = string("query_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14037952))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14169088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14696256))))[name = string("module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_249 = const()[name = string("op_249"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_249, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14712704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14843840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15371200))))[name = string("module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_253, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15387648))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15518784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16046144))))[name = string("module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_257 = const()[name = string("op_257"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_257, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16062592)))]; + tensor var_269_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_269_cast_fp16")]; + tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16064704)))]; + tensor var_271_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_271_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor var_273_to_fp16 = const()[name = string("op_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16066816)))]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_271_cast_fp16)[name = string("transpose_312")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_273_to_fp16)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_281 = const()[name = string("op_281"), val = tensor([1, 8, -1, 188])]; + tensor x_11_cast_fp16 = reshape(shape = var_281, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_285_begin_0 = const()[name = string("op_285_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_285_end_0 = const()[name = string("op_285_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_285_end_mask_0 = const()[name = string("op_285_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_285_cast_fp16 = slice_by_index(begin = var_285_begin_0, end = var_285_end_0, end_mask = var_285_end_mask_0, x = x_11_cast_fp16)[name = string("op_285_cast_fp16")]; + tensor var_286 = const()[name = string("op_286"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_286, x = var_285_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_269_cast_fp16)[name = string("transpose_311")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_295_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_295_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_295_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_3_axes_0 = const()[name = string("mask_3_axes_0"), val = tensor([1])]; + tensor mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = string("mask_3")]; + fp16 var_12_to_fp16 = const()[name = string("op_12_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = string("scores_3_cast_fp16")]; + tensor var_301_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_301_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_301_cast_fp16, cond = mask_3)[name = string("input_33_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_305_perm_0 = const()[name = string("op_305_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_306 = const()[name = string("op_306"), val = tensor([1, -1, 1024])]; + tensor var_305_cast_fp16 = transpose(perm = var_305_perm_0, x = x_13_cast_fp16)[name = string("transpose_308")]; + tensor input_35_cast_fp16 = reshape(shape = var_306, x = var_305_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16834880))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16966016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17493504))))[name = string("module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17509952)))]; + tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17512064)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_41_perm_0 = const()[name = string("input_41_perm_0"), val = tensor([0, 2, 1])]; + string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")]; + tensor input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor([1])]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0])]; + tensor input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor([1])]; + int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17514176))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17776384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18831360))))[name = string("module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = string("transpose_307")]; + tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; + tensor var_328 = expand_dims(axes = var_328_axes_0, x = pad_mask)[name = string("op_328")]; + tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_328)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("constant")]; + fp16 const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1024)]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1])]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([0, 0])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([1])]; + tensor const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18864192)))]; + tensor const_249_to_fp16 = const()[name = string("const_249_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18882688)))]; + tensor input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18884800))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19015936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19543744))))[name = string("module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_53_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_306")]; + tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor input_59_axes_0 = const()[name = string("input_59_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19560192)))]; + tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19562304)))]; + tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19564416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20088768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22198272))))[name = string("module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_59_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22263872))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24896064))))[name = string("module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_63_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_366_to_fp16 = const()[name = string("op_366_to_fp16"), val = fp16(0x1p-1)]; + tensor var_367_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_366_to_fp16)[name = string("op_367_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_367_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = string("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24912512)))]; + tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = string("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24914624)))]; + tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor input_73_axes_0 = const()[name = string("input_73_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24916736)))]; + tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24918848)))]; + tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24920960))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25445312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27557696))))[name = string("module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_73_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27623296))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28147648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30255744))))[name = string("module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_77_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p-1)]; + tensor var_396_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_396_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30272192)))]; + tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30274304)))]; + tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = string("query_3_cast_fp16")]; + tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30276416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30407552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30935104))))[name = string("module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_412, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30951552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31082688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31610112))))[name = string("module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_416 = const()[name = string("op_416"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_416, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31626560))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31757696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32284864))))[name = string("module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_420 = const()[name = string("op_420"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_420, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301312)))]; + tensor var_432_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_432_cast_fp16")]; + tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32303424)))]; + tensor var_434_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_434_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_29_transpose_x_0 = const()[name = string("x_29_transpose_x_0"), val = bool(false)]; + bool x_29_transpose_y_0 = const()[name = string("x_29_transpose_y_0"), val = bool(false)]; + tensor var_436_to_fp16 = const()[name = string("op_436_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32305536)))]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_434_cast_fp16)[name = string("transpose_305")]; + tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_436_to_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_31_mode_0 = const()[name = string("x_31_mode_0"), val = string("constant")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_444 = const()[name = string("op_444"), val = tensor([1, 8, -1, 188])]; + tensor x_33_cast_fp16 = reshape(shape = var_444, x = x_31_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_448_begin_0 = const()[name = string("op_448_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_448_end_0 = const()[name = string("op_448_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_448_end_mask_0 = const()[name = string("op_448_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_448_cast_fp16 = slice_by_index(begin = var_448_begin_0, end = var_448_end_0, end_mask = var_448_end_mask_0, x = x_33_cast_fp16)[name = string("op_448_cast_fp16")]; + tensor var_449 = const()[name = string("op_449"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_449, x = var_448_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_432_cast_fp16)[name = string("transpose_304")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_458_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_458_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_458_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = string("scores_7_cast_fp16")]; + tensor var_464_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_464_cast_fp16, cond = mask_3)[name = string("input_85_cast_fp16")]; + bool x_35_transpose_x_0 = const()[name = string("x_35_transpose_x_0"), val = bool(false)]; + bool x_35_transpose_y_0 = const()[name = string("x_35_transpose_y_0"), val = bool(false)]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")]; + tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_468_perm_0 = const()[name = string("op_468_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_469 = const()[name = string("op_469"), val = tensor([1, -1, 1024])]; + tensor var_468_cast_fp16 = transpose(perm = var_468_perm_0, x = x_35_cast_fp16)[name = string("transpose_301")]; + tensor input_87_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33073600))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33204736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33732608))))[name = string("module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_87_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33749056)))]; + tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33751168)))]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([1])]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([0, 0])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33753280))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34015488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35070464))))[name = string("module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = string("transpose_300")]; + tensor input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")]; + int32 x_41_split_num_splits_0 = const()[name = string("x_41_split_num_splits_0"), val = int32(2)]; + int32 x_41_split_axis_0 = const()[name = string("x_41_split_axis_0"), val = int32(1)]; + tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = string("x_41_split_cast_fp16")]; + tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = string("x_41_split_1_sigmoid_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_328)[name = string("input_97_cast_fp16")]; + tensor input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("constant")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1024)]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1])]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([0, 0])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1])]; + tensor const_250_to_fp16 = const()[name = string("const_250_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35103296)))]; + tensor const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35121792)))]; + tensor input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16, x = input_99_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_43_pad_type_0 = const()[name = string("x_43_pad_type_0"), val = string("valid")]; + tensor x_43_strides_0 = const()[name = string("x_43_strides_0"), val = tensor([1])]; + tensor x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor([0, 0])]; + tensor x_43_dilations_0 = const()[name = string("x_43_dilations_0"), val = tensor([1])]; + int32 x_43_groups_0 = const()[name = string("x_43_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35123904))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35255040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35783040))))[name = string("module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_105_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = string("transpose_299")]; + tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor input_111_axes_0 = const()[name = string("input_111_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35799488)))]; + tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35801600)))]; + tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35803712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36328064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38438720))))[name = string("module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_111_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38504320))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39028672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41136576))))[name = string("module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_115_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1p-1)]; + tensor var_530_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_530_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor input_123_axes_0 = const()[name = string("input_123_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = string("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41153024)))]; + tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = string("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41155136)))]; + tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41157248)))]; + tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41159360)))]; + tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41161472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41685824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43797248))))[name = string("module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_125_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43862848))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44387200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46495232))))[name = string("module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_129_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_558_to_fp16 = const()[name = string("op_558_to_fp16"), val = fp16(0x1p-1)]; + tensor var_559_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_558_to_fp16)[name = string("op_559_cast_fp16")]; + tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_559_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46511680)))]; + tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46513792)))]; + tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = string("query_5_cast_fp16")]; + tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46515904))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46647040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47174464))))[name = string("module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_575 = const()[name = string("op_575"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_575, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47190912))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47322048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47849664))))[name = string("module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_579 = const()[name = string("op_579"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_579, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47866112))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47997248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48524672))))[name = string("module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_583 = const()[name = string("op_583"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_583, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48541120)))]; + tensor var_595_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_595_cast_fp16")]; + tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48543232)))]; + tensor var_597_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_597_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_51_transpose_x_0 = const()[name = string("x_51_transpose_x_0"), val = bool(false)]; + bool x_51_transpose_y_0 = const()[name = string("x_51_transpose_y_0"), val = bool(false)]; + tensor var_599_to_fp16 = const()[name = string("op_599_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48545344)))]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_597_cast_fp16)[name = string("transpose_298")]; + tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_599_to_fp16)[name = string("x_51_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_607 = const()[name = string("op_607"), val = tensor([1, 8, -1, 188])]; + tensor x_55_cast_fp16 = reshape(shape = var_607, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_611_begin_0 = const()[name = string("op_611_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_611_end_0 = const()[name = string("op_611_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_611_end_mask_0 = const()[name = string("op_611_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_611_cast_fp16 = slice_by_index(begin = var_611_begin_0, end = var_611_end_0, end_mask = var_611_end_mask_0, x = x_55_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor var_612 = const()[name = string("op_612"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_612, x = var_611_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_595_cast_fp16)[name = string("transpose_297")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_621_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_621_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_621_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = string("scores_11_cast_fp16")]; + tensor var_627_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_627_cast_fp16, cond = mask_3)[name = string("input_137_cast_fp16")]; + bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)]; + bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)]; + tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")]; + tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_631_perm_0 = const()[name = string("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_632 = const()[name = string("op_632"), val = tensor([1, -1, 1024])]; + tensor var_631_cast_fp16 = transpose(perm = var_631_perm_0, x = x_57_cast_fp16)[name = string("transpose_294")]; + tensor input_139_cast_fp16 = reshape(shape = var_632, x = var_631_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49313408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49444544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49972032))))[name = string("module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_139_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49988480)))]; + tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49990592)))]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49992704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50254912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51310272))))[name = string("module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = string("transpose_293")]; + tensor input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_145_cast_fp16)[name = string("input_147_cast_fp16")]; + int32 x_63_split_num_splits_0 = const()[name = string("x_63_split_num_splits_0"), val = int32(2)]; + int32 x_63_split_axis_0 = const()[name = string("x_63_split_axis_0"), val = int32(1)]; + tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = string("x_63_split_cast_fp16")]; + tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = string("x_63_split_1_sigmoid_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_328)[name = string("input_149_cast_fp16")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_151_mode_0 = const()[name = string("input_151_mode_0"), val = string("constant")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1024)]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + tensor const_252_to_fp16 = const()[name = string("const_252_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51343104)))]; + tensor const_253_to_fp16 = const()[name = string("const_253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51361600)))]; + tensor input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16, x = input_151_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("valid")]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([0, 0])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51363712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51494848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52023104))))[name = string("module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_157_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = string("transpose_292")]; + tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = string("input_161_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52039552)))]; + tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52041664)))]; + tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52043776))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52568128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54679808))))[name = string("module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_163_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54745408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55269760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57377728))))[name = string("module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_167_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_692_to_fp16 = const()[name = string("op_692_to_fp16"), val = fp16(0x1p-1)]; + tensor var_693_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_692_to_fp16)[name = string("op_693_cast_fp16")]; + tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_693_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor input_175_axes_0 = const()[name = string("input_175_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = string("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57394176)))]; + tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = string("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57396288)))]; + tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor input_177_axes_0 = const()[name = string("input_177_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57398400)))]; + tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57400512)))]; + tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57402624))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57926976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60040640))))[name = string("module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_177_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60106240))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60630592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62738752))))[name = string("module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_181_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_721_to_fp16 = const()[name = string("op_721_to_fp16"), val = fp16(0x1p-1)]; + tensor var_722_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_721_to_fp16)[name = string("op_722_cast_fp16")]; + tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_722_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62755200)))]; + tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62757312)))]; + tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = string("query_7_cast_fp16")]; + tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62759424))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62890560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63418368))))[name = string("module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_738, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63434816))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63565952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64093632))))[name = string("module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_742, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64110080))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64241216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64768576))))[name = string("module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64785024)))]; + tensor var_758_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_758_cast_fp16")]; + tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64787136)))]; + tensor var_760_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_760_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_73_transpose_x_0 = const()[name = string("x_73_transpose_x_0"), val = bool(false)]; + bool x_73_transpose_y_0 = const()[name = string("x_73_transpose_y_0"), val = bool(false)]; + tensor var_762_to_fp16 = const()[name = string("op_762_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64789248)))]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_760_cast_fp16)[name = string("transpose_291")]; + tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_762_to_fp16)[name = string("x_73_cast_fp16")]; + tensor x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_770 = const()[name = string("op_770"), val = tensor([1, 8, -1, 188])]; + tensor x_77_cast_fp16 = reshape(shape = var_770, x = x_75_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_774_begin_0 = const()[name = string("op_774_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_774_end_0 = const()[name = string("op_774_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_774_end_mask_0 = const()[name = string("op_774_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = x_77_cast_fp16)[name = string("op_774_cast_fp16")]; + tensor var_775 = const()[name = string("op_775"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_758_cast_fp16)[name = string("transpose_290")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_784_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_784_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_784_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = string("scores_15_cast_fp16")]; + tensor var_790_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = string("op_790_cast_fp16")]; + tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_790_cast_fp16, cond = mask_3)[name = string("input_189_cast_fp16")]; + bool x_79_transpose_x_0 = const()[name = string("x_79_transpose_x_0"), val = bool(false)]; + bool x_79_transpose_y_0 = const()[name = string("x_79_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")]; + tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor var_794_perm_0 = const()[name = string("op_794_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_795 = const()[name = string("op_795"), val = tensor([1, -1, 1024])]; + tensor var_794_cast_fp16 = transpose(perm = var_794_perm_0, x = x_79_cast_fp16)[name = string("transpose_287")]; + tensor input_191_cast_fp16 = reshape(shape = var_795, x = var_794_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65557312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65688448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66216064))))[name = string("module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_191_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor x_83_axes_0 = const()[name = string("x_83_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66232512)))]; + tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66234624)))]; + tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_197_perm_0 = const()[name = string("input_197_perm_0"), val = tensor([0, 2, 1])]; + string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("valid")]; + tensor input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor([1])]; + tensor input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor([0, 0])]; + tensor input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor([1])]; + int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66236736))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66498944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67554880))))[name = string("module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = string("transpose_286")]; + tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_197_cast_fp16)[name = string("input_199_cast_fp16")]; + int32 x_85_split_num_splits_0 = const()[name = string("x_85_split_num_splits_0"), val = int32(2)]; + int32 x_85_split_axis_0 = const()[name = string("x_85_split_axis_0"), val = int32(1)]; + tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = string("x_85_split_cast_fp16")]; + tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = string("x_85_split_1_sigmoid_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_328)[name = string("input_201_cast_fp16")]; + tensor input_203_pad_0 = const()[name = string("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("constant")]; + fp16 const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = fp16(0x0p+0)]; + tensor input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = string("input_203_cast_fp16")]; + string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("valid")]; + int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1024)]; + tensor input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor([1])]; + tensor input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor([0, 0])]; + tensor input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor([1])]; + tensor const_254_to_fp16 = const()[name = string("const_254_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67587712)))]; + tensor const_255_to_fp16 = const()[name = string("const_255_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67606208)))]; + tensor input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16, x = input_203_cast_fp16)[name = string("input_207_cast_fp16")]; + tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = string("input_209_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("valid")]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67608320))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67739456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68268032))))[name = string("module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_209_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = string("transpose_285")]; + tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + tensor input_215_axes_0 = const()[name = string("input_215_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68284480)))]; + tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68286592)))]; + tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68288704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68813056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70925888))))[name = string("module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_215_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70991488))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71515840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73623872))))[name = string("module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_219_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)]; + tensor var_856_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_856_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor input_227_axes_0 = const()[name = string("input_227_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = string("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73640320)))]; + tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = string("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73642432)))]; + tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = string("input_227_cast_fp16")]; + tensor input_229_axes_0 = const()[name = string("input_229_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73644544)))]; + tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73646656)))]; + tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73648768))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74173120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76288192))))[name = string("module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_229_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76353792))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76878144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78986496))))[name = string("module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_233_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p-1)]; + tensor var_885_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_885_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79002944)))]; + tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79005056)))]; + tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = string("query_9_cast_fp16")]; + tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79007168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79138304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79666304))))[name = string("module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_901 = const()[name = string("op_901"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_901, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79682752))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79813888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80341696))))[name = string("module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_905 = const()[name = string("op_905"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_905, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80358144))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80489280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81016896))))[name = string("module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_909 = const()[name = string("op_909"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_909, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81033344)))]; + tensor var_921_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_921_cast_fp16")]; + tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81035456)))]; + tensor var_923_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_923_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_95_transpose_x_0 = const()[name = string("x_95_transpose_x_0"), val = bool(false)]; + bool x_95_transpose_y_0 = const()[name = string("x_95_transpose_y_0"), val = bool(false)]; + tensor var_925_to_fp16 = const()[name = string("op_925_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81037568)))]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_923_cast_fp16)[name = string("transpose_284")]; + tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_925_to_fp16)[name = string("x_95_cast_fp16")]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_97_mode_0 = const()[name = string("x_97_mode_0"), val = string("constant")]; + fp16 const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = fp16(0x0p+0)]; + tensor x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_933 = const()[name = string("op_933"), val = tensor([1, 8, -1, 188])]; + tensor x_99_cast_fp16 = reshape(shape = var_933, x = x_97_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor var_937_begin_0 = const()[name = string("op_937_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_937_end_0 = const()[name = string("op_937_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_937_end_mask_0 = const()[name = string("op_937_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_937_cast_fp16 = slice_by_index(begin = var_937_begin_0, end = var_937_end_0, end_mask = var_937_end_mask_0, x = x_99_cast_fp16)[name = string("op_937_cast_fp16")]; + tensor var_938 = const()[name = string("op_938"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_938, x = var_937_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_921_cast_fp16)[name = string("transpose_283")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_947_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_947_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_947_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = string("scores_19_cast_fp16")]; + tensor var_953_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = string("op_953_cast_fp16")]; + tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_953_cast_fp16, cond = mask_3)[name = string("input_241_cast_fp16")]; + bool x_101_transpose_x_0 = const()[name = string("x_101_transpose_x_0"), val = bool(false)]; + bool x_101_transpose_y_0 = const()[name = string("x_101_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")]; + tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_957_perm_0 = const()[name = string("op_957_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_958 = const()[name = string("op_958"), val = tensor([1, -1, 1024])]; + tensor var_957_cast_fp16 = transpose(perm = var_957_perm_0, x = x_101_cast_fp16)[name = string("transpose_280")]; + tensor input_243_cast_fp16 = reshape(shape = var_958, x = var_957_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81805632))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81936768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82463744))))[name = string("module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_243_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = string("input_247_cast_fp16")]; + tensor x_105_axes_0 = const()[name = string("x_105_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82480192)))]; + tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82482304)))]; + tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor input_249_perm_0 = const()[name = string("input_249_perm_0"), val = tensor([0, 2, 1])]; + string input_251_pad_type_0 = const()[name = string("input_251_pad_type_0"), val = string("valid")]; + tensor input_251_strides_0 = const()[name = string("input_251_strides_0"), val = tensor([1])]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0])]; + tensor input_251_dilations_0 = const()[name = string("input_251_dilations_0"), val = tensor([1])]; + int32 input_251_groups_0 = const()[name = string("input_251_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82484416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82746624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83802176))))[name = string("module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = string("transpose_279")]; + tensor input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + int32 x_107_split_num_splits_0 = const()[name = string("x_107_split_num_splits_0"), val = int32(2)]; + int32 x_107_split_axis_0 = const()[name = string("x_107_split_axis_0"), val = int32(1)]; + tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = string("x_107_split_cast_fp16")]; + tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = string("x_107_split_1_sigmoid_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_328)[name = string("input_253_cast_fp16")]; + tensor input_255_pad_0 = const()[name = string("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("constant")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = string("input_255_cast_fp16")]; + string input_257_pad_type_0 = const()[name = string("input_257_pad_type_0"), val = string("valid")]; + int32 input_257_groups_0 = const()[name = string("input_257_groups_0"), val = int32(1024)]; + tensor input_257_strides_0 = const()[name = string("input_257_strides_0"), val = tensor([1])]; + tensor input_257_pad_0 = const()[name = string("input_257_pad_0"), val = tensor([0, 0])]; + tensor input_257_dilations_0 = const()[name = string("input_257_dilations_0"), val = tensor([1])]; + tensor const_256_to_fp16 = const()[name = string("const_256_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83835008)))]; + tensor const_257_to_fp16 = const()[name = string("const_257_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83853504)))]; + tensor input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16, x = input_255_cast_fp16)[name = string("input_259_cast_fp16")]; + tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = string("input_261_cast_fp16")]; + string x_109_pad_type_0 = const()[name = string("x_109_pad_type_0"), val = string("valid")]; + tensor x_109_strides_0 = const()[name = string("x_109_strides_0"), val = tensor([1])]; + tensor x_109_pad_0 = const()[name = string("x_109_pad_0"), val = tensor([0, 0])]; + tensor x_109_dilations_0 = const()[name = string("x_109_dilations_0"), val = tensor([1])]; + int32 x_109_groups_0 = const()[name = string("x_109_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83855616))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83986752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84515904))))[name = string("module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_261_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = string("transpose_278")]; + tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor input_267_axes_0 = const()[name = string("input_267_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84532352)))]; + tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84534464)))]; + tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84536576))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85060928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87174208))))[name = string("module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_267_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87239808))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87764160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89872512))))[name = string("module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_271_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1019_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1019_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor input_279_axes_0 = const()[name = string("input_279_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = string("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89888960)))]; + tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = string("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89891072)))]; + tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = string("input_279_cast_fp16")]; + tensor input_281_axes_0 = const()[name = string("input_281_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89893184)))]; + tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89895296)))]; + tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = string("input_281_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89897408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90421760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92537920))))[name = string("module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_281_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92603520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93127872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95236544))))[name = string("module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_285_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1047_to_fp16 = const()[name = string("op_1047_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1048_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1047_to_fp16)[name = string("op_1048_cast_fp16")]; + tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1048_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95252992)))]; + tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95255104)))]; + tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = string("query_11_cast_fp16")]; + tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95257216))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95388352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95916160))))[name = string("module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1064 = const()[name = string("op_1064"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1064, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95932608))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96063744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96591424))))[name = string("module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1068 = const()[name = string("op_1068"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1068, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96607872))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96739008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97266048))))[name = string("module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1072 = const()[name = string("op_1072"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1072, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97282496)))]; + tensor var_1084_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1084_cast_fp16")]; + tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97284608)))]; + tensor var_1086_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1086_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97286720)))]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1086_cast_fp16)[name = string("transpose_277")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1088_to_fp16)[name = string("x_117_cast_fp16")]; + tensor x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor var_1096 = const()[name = string("op_1096"), val = tensor([1, 8, -1, 188])]; + tensor x_121_cast_fp16 = reshape(shape = var_1096, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor var_1100_begin_0 = const()[name = string("op_1100_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1100_end_0 = const()[name = string("op_1100_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1100_end_mask_0 = const()[name = string("op_1100_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1100_cast_fp16 = slice_by_index(begin = var_1100_begin_0, end = var_1100_end_0, end_mask = var_1100_end_mask_0, x = x_121_cast_fp16)[name = string("op_1100_cast_fp16")]; + tensor var_1101 = const()[name = string("op_1101"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1101, x = var_1100_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1084_cast_fp16)[name = string("transpose_276")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1110_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1110_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1110_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = string("scores_23_cast_fp16")]; + tensor var_1116_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = string("op_1116_cast_fp16")]; + tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1116_cast_fp16, cond = mask_3)[name = string("input_293_cast_fp16")]; + bool x_123_transpose_x_0 = const()[name = string("x_123_transpose_x_0"), val = bool(false)]; + bool x_123_transpose_y_0 = const()[name = string("x_123_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")]; + tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1120_perm_0 = const()[name = string("op_1120_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1121 = const()[name = string("op_1121"), val = tensor([1, -1, 1024])]; + tensor var_1120_cast_fp16 = transpose(perm = var_1120_perm_0, x = x_123_cast_fp16)[name = string("transpose_273")]; + tensor input_295_cast_fp16 = reshape(shape = var_1121, x = var_1120_cast_fp16)[name = string("input_295_cast_fp16")]; + tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98054784))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98185920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98712640))))[name = string("module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_295_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = string("input_299_cast_fp16")]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98729088)))]; + tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98731200)))]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_301_perm_0 = const()[name = string("input_301_perm_0"), val = tensor([0, 2, 1])]; + string input_303_pad_type_0 = const()[name = string("input_303_pad_type_0"), val = string("valid")]; + tensor input_303_strides_0 = const()[name = string("input_303_strides_0"), val = tensor([1])]; + tensor input_303_pad_0 = const()[name = string("input_303_pad_0"), val = tensor([0, 0])]; + tensor input_303_dilations_0 = const()[name = string("input_303_dilations_0"), val = tensor([1])]; + int32 input_303_groups_0 = const()[name = string("input_303_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98733312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98995520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100050368))))[name = string("module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = string("transpose_272")]; + tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_301_cast_fp16)[name = string("input_303_cast_fp16")]; + int32 x_129_split_num_splits_0 = const()[name = string("x_129_split_num_splits_0"), val = int32(2)]; + int32 x_129_split_axis_0 = const()[name = string("x_129_split_axis_0"), val = int32(1)]; + tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = string("x_129_split_cast_fp16")]; + tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = string("x_129_split_1_sigmoid_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_328)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("constant")]; + fp16 const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string input_309_pad_type_0 = const()[name = string("input_309_pad_type_0"), val = string("valid")]; + int32 input_309_groups_0 = const()[name = string("input_309_groups_0"), val = int32(1024)]; + tensor input_309_strides_0 = const()[name = string("input_309_strides_0"), val = tensor([1])]; + tensor input_309_pad_0 = const()[name = string("input_309_pad_0"), val = tensor([0, 0])]; + tensor input_309_dilations_0 = const()[name = string("input_309_dilations_0"), val = tensor([1])]; + tensor const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100083200)))]; + tensor const_259_to_fp16 = const()[name = string("const_259_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100101696)))]; + tensor input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16, x = input_307_cast_fp16)[name = string("input_311_cast_fp16")]; + tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = string("input_313_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("valid")]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([0, 0])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100103808))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100234944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100764672))))[name = string("module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_313_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = string("transpose_271")]; + tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_axes_0 = const()[name = string("input_319_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100781120)))]; + tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100783232)))]; + tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100785344))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101309696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103423424))))[name = string("module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_319_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103489024))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104013376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106122560))))[name = string("module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_323_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1182_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1181_to_fp16)[name = string("op_1182_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1182_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_axes_0 = const()[name = string("input_331_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = string("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106139008)))]; + tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = string("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106141120)))]; + tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + tensor input_333_axes_0 = const()[name = string("input_333_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106143232)))]; + tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106145344)))]; + tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = string("input_333_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106147456))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106671808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108784320))))[name = string("module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_333_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108849920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109374272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111482368))))[name = string("module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_337_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1210_to_fp16 = const()[name = string("op_1210_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1211_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1210_to_fp16)[name = string("op_1211_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1211_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111498816)))]; + tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111500928)))]; + tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = string("query_13_cast_fp16")]; + tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111503040))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111634176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112162240))))[name = string("module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1227 = const()[name = string("op_1227"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1227, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112178688))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112309824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112837376))))[name = string("module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1231 = const()[name = string("op_1231"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1231, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112853824))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112984960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113512448))))[name = string("module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1235 = const()[name = string("op_1235"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1235, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113528896)))]; + tensor var_1247_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1247_cast_fp16")]; + tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113531008)))]; + tensor var_1249_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1249_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_139_transpose_x_0 = const()[name = string("x_139_transpose_x_0"), val = bool(false)]; + bool x_139_transpose_y_0 = const()[name = string("x_139_transpose_y_0"), val = bool(false)]; + tensor var_1251_to_fp16 = const()[name = string("op_1251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113533120)))]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1249_cast_fp16)[name = string("transpose_270")]; + tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1251_to_fp16)[name = string("x_139_cast_fp16")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_141_mode_0 = const()[name = string("x_141_mode_0"), val = string("constant")]; + fp16 const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = fp16(0x0p+0)]; + tensor x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1259 = const()[name = string("op_1259"), val = tensor([1, 8, -1, 188])]; + tensor x_143_cast_fp16 = reshape(shape = var_1259, x = x_141_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1263_begin_0 = const()[name = string("op_1263_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1263_end_0 = const()[name = string("op_1263_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1263_end_mask_0 = const()[name = string("op_1263_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1263_cast_fp16 = slice_by_index(begin = var_1263_begin_0, end = var_1263_end_0, end_mask = var_1263_end_mask_0, x = x_143_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor var_1264 = const()[name = string("op_1264"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1264, x = var_1263_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1247_cast_fp16)[name = string("transpose_269")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1273_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1273_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1273_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = string("scores_27_cast_fp16")]; + tensor var_1279_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = string("op_1279_cast_fp16")]; + tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1279_cast_fp16, cond = mask_3)[name = string("input_345_cast_fp16")]; + bool x_145_transpose_x_0 = const()[name = string("x_145_transpose_x_0"), val = bool(false)]; + bool x_145_transpose_y_0 = const()[name = string("x_145_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")]; + tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1283_perm_0 = const()[name = string("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1284 = const()[name = string("op_1284"), val = tensor([1, -1, 1024])]; + tensor var_1283_cast_fp16 = transpose(perm = var_1283_perm_0, x = x_145_cast_fp16)[name = string("transpose_266")]; + tensor input_347_cast_fp16 = reshape(shape = var_1284, x = var_1283_cast_fp16)[name = string("input_347_cast_fp16")]; + tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114301184))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114432320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114959744))))[name = string("module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_347_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = string("input_351_cast_fp16")]; + tensor x_149_axes_0 = const()[name = string("x_149_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114976192)))]; + tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114978304)))]; + tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + string input_355_pad_type_0 = const()[name = string("input_355_pad_type_0"), val = string("valid")]; + tensor input_355_strides_0 = const()[name = string("input_355_strides_0"), val = tensor([1])]; + tensor input_355_pad_0 = const()[name = string("input_355_pad_0"), val = tensor([0, 0])]; + tensor input_355_dilations_0 = const()[name = string("input_355_dilations_0"), val = tensor([1])]; + int32 input_355_groups_0 = const()[name = string("input_355_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114980416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115242624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116297920))))[name = string("module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = string("transpose_265")]; + tensor input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_353_cast_fp16)[name = string("input_355_cast_fp16")]; + int32 x_151_split_num_splits_0 = const()[name = string("x_151_split_num_splits_0"), val = int32(2)]; + int32 x_151_split_axis_0 = const()[name = string("x_151_split_axis_0"), val = int32(1)]; + tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = string("x_151_split_cast_fp16")]; + tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = string("x_151_split_1_sigmoid_cast_fp16")]; + tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_328)[name = string("input_357_cast_fp16")]; + tensor input_359_pad_0 = const()[name = string("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_359_mode_0 = const()[name = string("input_359_mode_0"), val = string("constant")]; + fp16 const_77_to_fp16 = const()[name = string("const_77_to_fp16"), val = fp16(0x0p+0)]; + tensor input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = string("input_359_cast_fp16")]; + string input_361_pad_type_0 = const()[name = string("input_361_pad_type_0"), val = string("valid")]; + int32 input_361_groups_0 = const()[name = string("input_361_groups_0"), val = int32(1024)]; + tensor input_361_strides_0 = const()[name = string("input_361_strides_0"), val = tensor([1])]; + tensor input_361_pad_0 = const()[name = string("input_361_pad_0"), val = tensor([0, 0])]; + tensor input_361_dilations_0 = const()[name = string("input_361_dilations_0"), val = tensor([1])]; + tensor const_260_to_fp16 = const()[name = string("const_260_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116330752)))]; + tensor const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116349248)))]; + tensor input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16, x = input_359_cast_fp16)[name = string("input_363_cast_fp16")]; + tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = string("input_365_cast_fp16")]; + string x_153_pad_type_0 = const()[name = string("x_153_pad_type_0"), val = string("valid")]; + tensor x_153_strides_0 = const()[name = string("x_153_strides_0"), val = tensor([1])]; + tensor x_153_pad_0 = const()[name = string("x_153_pad_0"), val = tensor([0, 0])]; + tensor x_153_dilations_0 = const()[name = string("x_153_dilations_0"), val = tensor([1])]; + int32 x_153_groups_0 = const()[name = string("x_153_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116351360))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116482496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117010880))))[name = string("module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_365_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = string("transpose_264")]; + tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + tensor input_371_axes_0 = const()[name = string("input_371_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117027328)))]; + tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117029440)))]; + tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117031552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117555904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119667648))))[name = string("module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_371_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119733248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120257600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122365504))))[name = string("module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_375_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1345_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1345_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor input_383_axes_0 = const()[name = string("input_383_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = string("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122381952)))]; + tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = string("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122384064)))]; + tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = string("input_383_cast_fp16")]; + tensor input_385_axes_0 = const()[name = string("input_385_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122386176)))]; + tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122388288)))]; + tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = string("input_385_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122390400))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122914752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125028096))))[name = string("module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_385_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125093696))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125618048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127726016))))[name = string("module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_389_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1373_to_fp16 = const()[name = string("op_1373_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1374_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1373_to_fp16)[name = string("op_1374_cast_fp16")]; + tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1374_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127742464)))]; + tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127744576)))]; + tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = string("query_15_cast_fp16")]; + tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127746688))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127877824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128405952))))[name = string("module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1390 = const()[name = string("op_1390"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1390, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128422400))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128553536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129081152))))[name = string("module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1394 = const()[name = string("op_1394"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1394, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129097600))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129228736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129756160))))[name = string("module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1398 = const()[name = string("op_1398"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1398, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129772608)))]; + tensor var_1410_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1410_cast_fp16")]; + tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129774720)))]; + tensor var_1412_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1412_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_161_transpose_x_0 = const()[name = string("x_161_transpose_x_0"), val = bool(false)]; + bool x_161_transpose_y_0 = const()[name = string("x_161_transpose_y_0"), val = bool(false)]; + tensor var_1414_to_fp16 = const()[name = string("op_1414_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129776832)))]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1412_cast_fp16)[name = string("transpose_263")]; + tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1414_to_fp16)[name = string("x_161_cast_fp16")]; + tensor x_163_pad_0 = const()[name = string("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_163_mode_0 = const()[name = string("x_163_mode_0"), val = string("constant")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor var_1422 = const()[name = string("op_1422"), val = tensor([1, 8, -1, 188])]; + tensor x_165_cast_fp16 = reshape(shape = var_1422, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1426_begin_0 = const()[name = string("op_1426_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1426_end_0 = const()[name = string("op_1426_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1426_end_mask_0 = const()[name = string("op_1426_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = x_165_cast_fp16)[name = string("op_1426_cast_fp16")]; + tensor var_1427 = const()[name = string("op_1427"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1427, x = var_1426_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1410_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_1436_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1436_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_1436_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = string("scores_31_cast_fp16")]; + tensor var_1442_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = string("op_1442_cast_fp16")]; + tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1442_cast_fp16, cond = mask_3)[name = string("input_397_cast_fp16")]; + bool x_167_transpose_x_0 = const()[name = string("x_167_transpose_x_0"), val = bool(false)]; + bool x_167_transpose_y_0 = const()[name = string("x_167_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")]; + tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1446_perm_0 = const()[name = string("op_1446_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1447 = const()[name = string("op_1447"), val = tensor([1, -1, 1024])]; + tensor var_1446_cast_fp16 = transpose(perm = var_1446_perm_0, x = x_167_cast_fp16)[name = string("transpose_259")]; + tensor input_399_cast_fp16 = reshape(shape = var_1447, x = var_1446_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130544896))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130676032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131203648))))[name = string("module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_399_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = string("input_403_cast_fp16")]; + tensor x_171_axes_0 = const()[name = string("x_171_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131220096)))]; + tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131222208)))]; + tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string input_407_pad_type_0 = const()[name = string("input_407_pad_type_0"), val = string("valid")]; + tensor input_407_strides_0 = const()[name = string("input_407_strides_0"), val = tensor([1])]; + tensor input_407_pad_0 = const()[name = string("input_407_pad_0"), val = tensor([0, 0])]; + tensor input_407_dilations_0 = const()[name = string("input_407_dilations_0"), val = tensor([1])]; + int32 input_407_groups_0 = const()[name = string("input_407_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131224320))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131486528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132541504))))[name = string("module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = string("transpose_258")]; + tensor input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_405_cast_fp16)[name = string("input_407_cast_fp16")]; + int32 x_173_split_num_splits_0 = const()[name = string("x_173_split_num_splits_0"), val = int32(2)]; + int32 x_173_split_axis_0 = const()[name = string("x_173_split_axis_0"), val = int32(1)]; + tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = string("x_173_split_cast_fp16")]; + tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = string("x_173_split_1_sigmoid_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_328)[name = string("input_409_cast_fp16")]; + tensor input_411_pad_0 = const()[name = string("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_411_mode_0 = const()[name = string("input_411_mode_0"), val = string("constant")]; + fp16 const_87_to_fp16 = const()[name = string("const_87_to_fp16"), val = fp16(0x0p+0)]; + tensor input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = string("input_411_cast_fp16")]; + string input_413_pad_type_0 = const()[name = string("input_413_pad_type_0"), val = string("valid")]; + int32 input_413_groups_0 = const()[name = string("input_413_groups_0"), val = int32(1024)]; + tensor input_413_strides_0 = const()[name = string("input_413_strides_0"), val = tensor([1])]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0])]; + tensor input_413_dilations_0 = const()[name = string("input_413_dilations_0"), val = tensor([1])]; + tensor const_262_to_fp16 = const()[name = string("const_262_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132574336)))]; + tensor const_263_to_fp16 = const()[name = string("const_263_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132592832)))]; + tensor input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16, x = input_411_cast_fp16)[name = string("input_415_cast_fp16")]; + tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = string("input_417_cast_fp16")]; + string x_175_pad_type_0 = const()[name = string("x_175_pad_type_0"), val = string("valid")]; + tensor x_175_strides_0 = const()[name = string("x_175_strides_0"), val = tensor([1])]; + tensor x_175_pad_0 = const()[name = string("x_175_pad_0"), val = tensor([0, 0])]; + tensor x_175_dilations_0 = const()[name = string("x_175_dilations_0"), val = tensor([1])]; + int32 x_175_groups_0 = const()[name = string("x_175_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132594944))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132726080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133254400))))[name = string("module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_417_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = string("transpose_257")]; + tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = string("input_421_cast_fp16")]; + tensor input_423_axes_0 = const()[name = string("input_423_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133270848)))]; + tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133272960)))]; + tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133275072))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133799424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135911680))))[name = string("module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_423_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135977280))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136501632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138609600))))[name = string("module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_427_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1508_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1508_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor input_435_axes_0 = const()[name = string("input_435_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = string("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138626048)))]; + tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = string("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138628160)))]; + tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = string("input_435_cast_fp16")]; + tensor input_437_axes_0 = const()[name = string("input_437_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138630272)))]; + tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138632384)))]; + tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = string("input_437_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138634496))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139158848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141273024))))[name = string("module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_437_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141338624))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141862976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143971072))))[name = string("module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_441_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_1536_to_fp16 = const()[name = string("op_1536_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1537_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1536_to_fp16)[name = string("op_1537_cast_fp16")]; + tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1537_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143987520)))]; + tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143989632)))]; + tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = string("query_17_cast_fp16")]; + tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143991744))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144122880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144650368))))[name = string("module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_1553 = const()[name = string("op_1553"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_1553, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144666816))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144797952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145325440))))[name = string("module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_1557 = const()[name = string("op_1557"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_1557, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145341888))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145473024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146000192))))[name = string("module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_1561 = const()[name = string("op_1561"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_1561, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146016640)))]; + tensor var_1573_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1573_cast_fp16")]; + tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146018752)))]; + tensor var_1575_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1575_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_183_transpose_x_0 = const()[name = string("x_183_transpose_x_0"), val = bool(false)]; + bool x_183_transpose_y_0 = const()[name = string("x_183_transpose_y_0"), val = bool(false)]; + tensor var_1577_to_fp16 = const()[name = string("op_1577_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146020864)))]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1575_cast_fp16)[name = string("transpose_256")]; + tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1577_to_fp16)[name = string("x_183_cast_fp16")]; + tensor x_185_pad_0 = const()[name = string("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_185_mode_0 = const()[name = string("x_185_mode_0"), val = string("constant")]; + fp16 const_94_to_fp16 = const()[name = string("const_94_to_fp16"), val = fp16(0x0p+0)]; + tensor x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor var_1585 = const()[name = string("op_1585"), val = tensor([1, 8, -1, 188])]; + tensor x_187_cast_fp16 = reshape(shape = var_1585, x = x_185_cast_fp16)[name = string("x_187_cast_fp16")]; + tensor var_1589_begin_0 = const()[name = string("op_1589_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1589_end_0 = const()[name = string("op_1589_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1589_end_mask_0 = const()[name = string("op_1589_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1589_cast_fp16 = slice_by_index(begin = var_1589_begin_0, end = var_1589_end_0, end_mask = var_1589_end_mask_0, x = x_187_cast_fp16)[name = string("op_1589_cast_fp16")]; + tensor var_1590 = const()[name = string("op_1590"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1590, x = var_1589_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1573_cast_fp16)[name = string("transpose_255")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_1599_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1599_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_1599_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = string("scores_35_cast_fp16")]; + tensor var_1605_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = string("op_1605_cast_fp16")]; + tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1605_cast_fp16, cond = mask_3)[name = string("input_449_cast_fp16")]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = string("x_189_cast_fp16")]; + tensor var_1609_perm_0 = const()[name = string("op_1609_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1610 = const()[name = string("op_1610"), val = tensor([1, -1, 1024])]; + tensor var_1609_cast_fp16 = transpose(perm = var_1609_perm_0, x = x_189_cast_fp16)[name = string("transpose_252")]; + tensor input_451_cast_fp16 = reshape(shape = var_1610, x = var_1609_cast_fp16)[name = string("input_451_cast_fp16")]; + tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146788928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146920064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147446912))))[name = string("module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_451_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = string("input_455_cast_fp16")]; + tensor x_193_axes_0 = const()[name = string("x_193_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147463360)))]; + tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147465472)))]; + tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor input_457_perm_0 = const()[name = string("input_457_perm_0"), val = tensor([0, 2, 1])]; + string input_459_pad_type_0 = const()[name = string("input_459_pad_type_0"), val = string("valid")]; + tensor input_459_strides_0 = const()[name = string("input_459_strides_0"), val = tensor([1])]; + tensor input_459_pad_0 = const()[name = string("input_459_pad_0"), val = tensor([0, 0])]; + tensor input_459_dilations_0 = const()[name = string("input_459_dilations_0"), val = tensor([1])]; + int32 input_459_groups_0 = const()[name = string("input_459_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147467584))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147729792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148784320))))[name = string("module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = string("transpose_251")]; + tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_457_cast_fp16)[name = string("input_459_cast_fp16")]; + int32 x_195_split_num_splits_0 = const()[name = string("x_195_split_num_splits_0"), val = int32(2)]; + int32 x_195_split_axis_0 = const()[name = string("x_195_split_axis_0"), val = int32(1)]; + tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = string("x_195_split_cast_fp16")]; + tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = string("x_195_split_1_sigmoid_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_328)[name = string("input_461_cast_fp16")]; + tensor input_463_pad_0 = const()[name = string("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_463_mode_0 = const()[name = string("input_463_mode_0"), val = string("constant")]; + fp16 const_97_to_fp16 = const()[name = string("const_97_to_fp16"), val = fp16(0x0p+0)]; + tensor input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = string("input_463_cast_fp16")]; + string input_465_pad_type_0 = const()[name = string("input_465_pad_type_0"), val = string("valid")]; + int32 input_465_groups_0 = const()[name = string("input_465_groups_0"), val = int32(1024)]; + tensor input_465_strides_0 = const()[name = string("input_465_strides_0"), val = tensor([1])]; + tensor input_465_pad_0 = const()[name = string("input_465_pad_0"), val = tensor([0, 0])]; + tensor input_465_dilations_0 = const()[name = string("input_465_dilations_0"), val = tensor([1])]; + tensor const_264_to_fp16 = const()[name = string("const_264_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148817152)))]; + tensor const_265_to_fp16 = const()[name = string("const_265_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148835648)))]; + tensor input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16, x = input_463_cast_fp16)[name = string("input_467_cast_fp16")]; + tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = string("input_469_cast_fp16")]; + string x_197_pad_type_0 = const()[name = string("x_197_pad_type_0"), val = string("valid")]; + tensor x_197_strides_0 = const()[name = string("x_197_strides_0"), val = tensor([1])]; + tensor x_197_pad_0 = const()[name = string("x_197_pad_0"), val = tensor([0, 0])]; + tensor x_197_dilations_0 = const()[name = string("x_197_dilations_0"), val = tensor([1])]; + int32 x_197_groups_0 = const()[name = string("x_197_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148837760))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148968896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149497472))))[name = string("module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_469_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = string("transpose_250")]; + tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = string("input_473_cast_fp16")]; + tensor input_475_axes_0 = const()[name = string("input_475_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149513920)))]; + tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149516032)))]; + tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149518144))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150042496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152154752))))[name = string("module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_475_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152220352))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152744704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154852672))))[name = string("module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_479_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_1670_to_fp16 = const()[name = string("op_1670_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1671_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1670_to_fp16)[name = string("op_1671_cast_fp16")]; + tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1671_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor input_487_axes_0 = const()[name = string("input_487_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = string("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154869120)))]; + tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = string("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154871232)))]; + tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = string("input_487_cast_fp16")]; + tensor input_489_axes_0 = const()[name = string("input_489_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154873344)))]; + tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154875456)))]; + tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = string("input_489_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154877568))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155401920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157515648))))[name = string("module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_489_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157581248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158105600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160213696))))[name = string("module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_493_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_1699_to_fp16 = const()[name = string("op_1699_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1700_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1699_to_fp16)[name = string("op_1700_cast_fp16")]; + tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1700_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160230144)))]; + tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160232256)))]; + tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = string("query_19_cast_fp16")]; + tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160234368))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160365504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160893376))))[name = string("module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_1716 = const()[name = string("op_1716"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_1716, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160909824))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161040960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161568576))))[name = string("module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_1720 = const()[name = string("op_1720"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_1720, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161585024))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161716160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162243776))))[name = string("module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_1724 = const()[name = string("op_1724"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_1724, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162260224)))]; + tensor var_1736_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_1736_cast_fp16")]; + tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162262336)))]; + tensor var_1738_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_1738_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_205_transpose_x_0 = const()[name = string("x_205_transpose_x_0"), val = bool(false)]; + bool x_205_transpose_y_0 = const()[name = string("x_205_transpose_y_0"), val = bool(false)]; + tensor var_1740_to_fp16 = const()[name = string("op_1740_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162264448)))]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1738_cast_fp16)[name = string("transpose_249")]; + tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1740_to_fp16)[name = string("x_205_cast_fp16")]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_207_mode_0 = const()[name = string("x_207_mode_0"), val = string("constant")]; + fp16 const_104_to_fp16 = const()[name = string("const_104_to_fp16"), val = fp16(0x0p+0)]; + tensor x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor var_1748 = const()[name = string("op_1748"), val = tensor([1, 8, -1, 188])]; + tensor x_209_cast_fp16 = reshape(shape = var_1748, x = x_207_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor var_1752_begin_0 = const()[name = string("op_1752_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1752_end_0 = const()[name = string("op_1752_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1752_end_mask_0 = const()[name = string("op_1752_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1752_cast_fp16 = slice_by_index(begin = var_1752_begin_0, end = var_1752_end_0, end_mask = var_1752_end_mask_0, x = x_209_cast_fp16)[name = string("op_1752_cast_fp16")]; + tensor var_1753 = const()[name = string("op_1753"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1753, x = var_1752_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1736_cast_fp16)[name = string("transpose_248")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_1762_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_1762_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_1762_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = string("scores_39_cast_fp16")]; + tensor var_1768_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = string("op_1768_cast_fp16")]; + tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1768_cast_fp16, cond = mask_3)[name = string("input_501_cast_fp16")]; + bool x_211_transpose_x_0 = const()[name = string("x_211_transpose_x_0"), val = bool(false)]; + bool x_211_transpose_y_0 = const()[name = string("x_211_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")]; + tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_1772_perm_0 = const()[name = string("op_1772_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1773 = const()[name = string("op_1773"), val = tensor([1, -1, 1024])]; + tensor var_1772_cast_fp16 = transpose(perm = var_1772_perm_0, x = x_211_cast_fp16)[name = string("transpose_245")]; + tensor input_503_cast_fp16 = reshape(shape = var_1773, x = var_1772_cast_fp16)[name = string("input_503_cast_fp16")]; + tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163032512))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163163648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163691200))))[name = string("module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_503_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = string("input_507_cast_fp16")]; + tensor x_215_axes_0 = const()[name = string("x_215_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163707648)))]; + tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163709760)))]; + tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor input_509_perm_0 = const()[name = string("input_509_perm_0"), val = tensor([0, 2, 1])]; + string input_511_pad_type_0 = const()[name = string("input_511_pad_type_0"), val = string("valid")]; + tensor input_511_strides_0 = const()[name = string("input_511_strides_0"), val = tensor([1])]; + tensor input_511_pad_0 = const()[name = string("input_511_pad_0"), val = tensor([0, 0])]; + tensor input_511_dilations_0 = const()[name = string("input_511_dilations_0"), val = tensor([1])]; + int32 input_511_groups_0 = const()[name = string("input_511_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163711872))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163974080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165029696))))[name = string("module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = string("transpose_244")]; + tensor input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_509_cast_fp16)[name = string("input_511_cast_fp16")]; + int32 x_217_split_num_splits_0 = const()[name = string("x_217_split_num_splits_0"), val = int32(2)]; + int32 x_217_split_axis_0 = const()[name = string("x_217_split_axis_0"), val = int32(1)]; + tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = string("x_217_split_cast_fp16")]; + tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = string("x_217_split_1_sigmoid_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_328)[name = string("input_513_cast_fp16")]; + tensor input_515_pad_0 = const()[name = string("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_515_mode_0 = const()[name = string("input_515_mode_0"), val = string("constant")]; + fp16 const_107_to_fp16 = const()[name = string("const_107_to_fp16"), val = fp16(0x0p+0)]; + tensor input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = string("input_515_cast_fp16")]; + string input_517_pad_type_0 = const()[name = string("input_517_pad_type_0"), val = string("valid")]; + int32 input_517_groups_0 = const()[name = string("input_517_groups_0"), val = int32(1024)]; + tensor input_517_strides_0 = const()[name = string("input_517_strides_0"), val = tensor([1])]; + tensor input_517_pad_0 = const()[name = string("input_517_pad_0"), val = tensor([0, 0])]; + tensor input_517_dilations_0 = const()[name = string("input_517_dilations_0"), val = tensor([1])]; + tensor const_266_to_fp16 = const()[name = string("const_266_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165062528)))]; + tensor const_267_to_fp16 = const()[name = string("const_267_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165081024)))]; + tensor input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16, x = input_515_cast_fp16)[name = string("input_519_cast_fp16")]; + tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = string("input_521_cast_fp16")]; + string x_219_pad_type_0 = const()[name = string("x_219_pad_type_0"), val = string("valid")]; + tensor x_219_strides_0 = const()[name = string("x_219_strides_0"), val = tensor([1])]; + tensor x_219_pad_0 = const()[name = string("x_219_pad_0"), val = tensor([0, 0])]; + tensor x_219_dilations_0 = const()[name = string("x_219_dilations_0"), val = tensor([1])]; + int32 x_219_groups_0 = const()[name = string("x_219_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165083136))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165214272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165742848))))[name = string("module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_521_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = string("transpose_243")]; + tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = string("input_525_cast_fp16")]; + tensor input_527_axes_0 = const()[name = string("input_527_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165759296)))]; + tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165761408)))]; + tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165763520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166287872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168401920))))[name = string("module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_527_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168467520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168991872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171099840))))[name = string("module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_531_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_1833_to_fp16 = const()[name = string("op_1833_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1834_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1833_to_fp16)[name = string("op_1834_cast_fp16")]; + tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1834_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor input_539_axes_0 = const()[name = string("input_539_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = string("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171116288)))]; + tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = string("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171118400)))]; + tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = string("input_539_cast_fp16")]; + tensor input_541_axes_0 = const()[name = string("input_541_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171120512)))]; + tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171122624)))]; + tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = string("input_541_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171124736))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171649088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173765952))))[name = string("module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_541_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173831552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174355904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176465152))))[name = string("module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_545_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_1862_to_fp16 = const()[name = string("op_1862_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1863_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1862_to_fp16)[name = string("op_1863_cast_fp16")]; + tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1863_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176481600)))]; + tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176483712)))]; + tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = string("query_21_cast_fp16")]; + tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176485824))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176616960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177144960))))[name = string("module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_1879 = const()[name = string("op_1879"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_1879, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177161408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177292544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177820480))))[name = string("module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_1883 = const()[name = string("op_1883"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_1883, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177836928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177968064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178494592))))[name = string("module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_1887 = const()[name = string("op_1887"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_1887, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178511040)))]; + tensor var_1899_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_1899_cast_fp16")]; + tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178513152)))]; + tensor var_1901_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_1901_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_227_transpose_x_0 = const()[name = string("x_227_transpose_x_0"), val = bool(false)]; + bool x_227_transpose_y_0 = const()[name = string("x_227_transpose_y_0"), val = bool(false)]; + tensor var_1903_to_fp16 = const()[name = string("op_1903_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178515264)))]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1901_cast_fp16)[name = string("transpose_242")]; + tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_1903_to_fp16)[name = string("x_227_cast_fp16")]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_229_mode_0 = const()[name = string("x_229_mode_0"), val = string("constant")]; + fp16 const_114_to_fp16 = const()[name = string("const_114_to_fp16"), val = fp16(0x0p+0)]; + tensor x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor var_1911 = const()[name = string("op_1911"), val = tensor([1, 8, -1, 188])]; + tensor x_231_cast_fp16 = reshape(shape = var_1911, x = x_229_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor var_1915_begin_0 = const()[name = string("op_1915_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1915_end_0 = const()[name = string("op_1915_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1915_end_mask_0 = const()[name = string("op_1915_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1915_cast_fp16 = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = x_231_cast_fp16)[name = string("op_1915_cast_fp16")]; + tensor var_1916 = const()[name = string("op_1916"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_1916, x = var_1915_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1899_cast_fp16)[name = string("transpose_241")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_1925_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_1925_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_1925_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = string("scores_43_cast_fp16")]; + tensor var_1931_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = string("op_1931_cast_fp16")]; + tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_1931_cast_fp16, cond = mask_3)[name = string("input_553_cast_fp16")]; + bool x_233_transpose_x_0 = const()[name = string("x_233_transpose_x_0"), val = bool(false)]; + bool x_233_transpose_y_0 = const()[name = string("x_233_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")]; + tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor var_1935_perm_0 = const()[name = string("op_1935_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1936 = const()[name = string("op_1936"), val = tensor([1, -1, 1024])]; + tensor var_1935_cast_fp16 = transpose(perm = var_1935_perm_0, x = x_233_cast_fp16)[name = string("transpose_238")]; + tensor input_555_cast_fp16 = reshape(shape = var_1936, x = var_1935_cast_fp16)[name = string("input_555_cast_fp16")]; + tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179283328))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179414464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179941888))))[name = string("module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_555_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = string("input_559_cast_fp16")]; + tensor x_237_axes_0 = const()[name = string("x_237_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179958336)))]; + tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179960448)))]; + tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = string("x_237_cast_fp16")]; + tensor input_561_perm_0 = const()[name = string("input_561_perm_0"), val = tensor([0, 2, 1])]; + string input_563_pad_type_0 = const()[name = string("input_563_pad_type_0"), val = string("valid")]; + tensor input_563_strides_0 = const()[name = string("input_563_strides_0"), val = tensor([1])]; + tensor input_563_pad_0 = const()[name = string("input_563_pad_0"), val = tensor([0, 0])]; + tensor input_563_dilations_0 = const()[name = string("input_563_dilations_0"), val = tensor([1])]; + int32 input_563_groups_0 = const()[name = string("input_563_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179962560))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180224768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181279872))))[name = string("module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = string("transpose_237")]; + tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_561_cast_fp16)[name = string("input_563_cast_fp16")]; + int32 x_239_split_num_splits_0 = const()[name = string("x_239_split_num_splits_0"), val = int32(2)]; + int32 x_239_split_axis_0 = const()[name = string("x_239_split_axis_0"), val = int32(1)]; + tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = string("x_239_split_cast_fp16")]; + tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = string("x_239_split_1_sigmoid_cast_fp16")]; + tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = string("x_239_cast_fp16")]; + tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_328)[name = string("input_565_cast_fp16")]; + tensor input_567_pad_0 = const()[name = string("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_567_mode_0 = const()[name = string("input_567_mode_0"), val = string("constant")]; + fp16 const_117_to_fp16 = const()[name = string("const_117_to_fp16"), val = fp16(0x0p+0)]; + tensor input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = string("input_567_cast_fp16")]; + string input_569_pad_type_0 = const()[name = string("input_569_pad_type_0"), val = string("valid")]; + int32 input_569_groups_0 = const()[name = string("input_569_groups_0"), val = int32(1024)]; + tensor input_569_strides_0 = const()[name = string("input_569_strides_0"), val = tensor([1])]; + tensor input_569_pad_0 = const()[name = string("input_569_pad_0"), val = tensor([0, 0])]; + tensor input_569_dilations_0 = const()[name = string("input_569_dilations_0"), val = tensor([1])]; + tensor const_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181312704)))]; + tensor const_269_to_fp16 = const()[name = string("const_269_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181331200)))]; + tensor input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16, x = input_567_cast_fp16)[name = string("input_571_cast_fp16")]; + tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = string("input_573_cast_fp16")]; + string x_241_pad_type_0 = const()[name = string("x_241_pad_type_0"), val = string("valid")]; + tensor x_241_strides_0 = const()[name = string("x_241_strides_0"), val = tensor([1])]; + tensor x_241_pad_0 = const()[name = string("x_241_pad_0"), val = tensor([0, 0])]; + tensor x_241_dilations_0 = const()[name = string("x_241_dilations_0"), val = tensor([1])]; + int32 x_241_groups_0 = const()[name = string("x_241_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181333312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181464448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181994368))))[name = string("module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_573_cast_fp16)[name = string("x_241_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = string("transpose_236")]; + tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = string("input_577_cast_fp16")]; + tensor input_579_axes_0 = const()[name = string("input_579_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182010816)))]; + tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182012928)))]; + tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182015040))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182539392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184656768))))[name = string("module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_579_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184722368))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185246720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187355328))))[name = string("module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_583_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_1996_to_fp16 = const()[name = string("op_1996_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1997_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1996_to_fp16)[name = string("op_1997_cast_fp16")]; + tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1997_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor input_591_axes_0 = const()[name = string("input_591_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = string("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187371776)))]; + tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = string("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187373888)))]; + tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = string("input_591_cast_fp16")]; + tensor input_593_axes_0 = const()[name = string("input_593_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187376000)))]; + tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187378112)))]; + tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = string("input_593_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187380224))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187904576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190022400))))[name = string("module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_593_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190088000))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190612352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192721728))))[name = string("module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_597_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2025_to_fp16 = const()[name = string("op_2025_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2026_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2025_to_fp16)[name = string("op_2026_cast_fp16")]; + tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2026_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192738176)))]; + tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192740288)))]; + tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = string("query_23_cast_fp16")]; + tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192742400))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192873536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193401024))))[name = string("module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2042 = const()[name = string("op_2042"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2042, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193417472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193548608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194076672))))[name = string("module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2046 = const()[name = string("op_2046"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2046, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194093120))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194224256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194749760))))[name = string("module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2050 = const()[name = string("op_2050"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2050, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194766208)))]; + tensor var_2062_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2062_cast_fp16")]; + tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194768320)))]; + tensor var_2064_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2064_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_249_transpose_x_0 = const()[name = string("x_249_transpose_x_0"), val = bool(false)]; + bool x_249_transpose_y_0 = const()[name = string("x_249_transpose_y_0"), val = bool(false)]; + tensor var_2066_to_fp16 = const()[name = string("op_2066_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194770432)))]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2064_cast_fp16)[name = string("transpose_235")]; + tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2066_to_fp16)[name = string("x_249_cast_fp16")]; + tensor x_251_pad_0 = const()[name = string("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_251_mode_0 = const()[name = string("x_251_mode_0"), val = string("constant")]; + fp16 const_124_to_fp16 = const()[name = string("const_124_to_fp16"), val = fp16(0x0p+0)]; + tensor x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor var_2074 = const()[name = string("op_2074"), val = tensor([1, 8, -1, 188])]; + tensor x_253_cast_fp16 = reshape(shape = var_2074, x = x_251_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor var_2078_begin_0 = const()[name = string("op_2078_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2078_end_0 = const()[name = string("op_2078_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2078_end_mask_0 = const()[name = string("op_2078_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = x_253_cast_fp16)[name = string("op_2078_cast_fp16")]; + tensor var_2079 = const()[name = string("op_2079"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2079, x = var_2078_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2062_cast_fp16)[name = string("transpose_234")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2088_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2088_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2088_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = string("scores_47_cast_fp16")]; + tensor var_2094_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2094_cast_fp16, cond = mask_3)[name = string("input_605_cast_fp16")]; + bool x_255_transpose_x_0 = const()[name = string("x_255_transpose_x_0"), val = bool(false)]; + bool x_255_transpose_y_0 = const()[name = string("x_255_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")]; + tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2099 = const()[name = string("op_2099"), val = tensor([1, -1, 1024])]; + tensor var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = x_255_cast_fp16)[name = string("transpose_231")]; + tensor input_607_cast_fp16 = reshape(shape = var_2099, x = var_2098_cast_fp16)[name = string("input_607_cast_fp16")]; + tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195538496))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195669632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196197440))))[name = string("module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_607_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = string("input_611_cast_fp16")]; + tensor x_259_axes_0 = const()[name = string("x_259_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196213888)))]; + tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196216000)))]; + tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_613_perm_0 = const()[name = string("input_613_perm_0"), val = tensor([0, 2, 1])]; + string input_615_pad_type_0 = const()[name = string("input_615_pad_type_0"), val = string("valid")]; + tensor input_615_strides_0 = const()[name = string("input_615_strides_0"), val = tensor([1])]; + tensor input_615_pad_0 = const()[name = string("input_615_pad_0"), val = tensor([0, 0])]; + tensor input_615_dilations_0 = const()[name = string("input_615_dilations_0"), val = tensor([1])]; + int32 input_615_groups_0 = const()[name = string("input_615_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196218112))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196480320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197535168))))[name = string("module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = string("transpose_230")]; + tensor input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_613_cast_fp16)[name = string("input_615_cast_fp16")]; + int32 x_261_split_num_splits_0 = const()[name = string("x_261_split_num_splits_0"), val = int32(2)]; + int32 x_261_split_axis_0 = const()[name = string("x_261_split_axis_0"), val = int32(1)]; + tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = string("x_261_split_cast_fp16")]; + tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = string("x_261_split_1_sigmoid_cast_fp16")]; + tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = string("x_261_cast_fp16")]; + tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_328)[name = string("input_617_cast_fp16")]; + tensor input_619_pad_0 = const()[name = string("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_619_mode_0 = const()[name = string("input_619_mode_0"), val = string("constant")]; + fp16 const_127_to_fp16 = const()[name = string("const_127_to_fp16"), val = fp16(0x0p+0)]; + tensor input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = string("input_619_cast_fp16")]; + string input_621_pad_type_0 = const()[name = string("input_621_pad_type_0"), val = string("valid")]; + int32 input_621_groups_0 = const()[name = string("input_621_groups_0"), val = int32(1024)]; + tensor input_621_strides_0 = const()[name = string("input_621_strides_0"), val = tensor([1])]; + tensor input_621_pad_0 = const()[name = string("input_621_pad_0"), val = tensor([0, 0])]; + tensor input_621_dilations_0 = const()[name = string("input_621_dilations_0"), val = tensor([1])]; + tensor const_270_to_fp16 = const()[name = string("const_270_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197568000)))]; + tensor const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197586496)))]; + tensor input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16, x = input_619_cast_fp16)[name = string("input_623_cast_fp16")]; + tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = string("input_625_cast_fp16")]; + string x_263_pad_type_0 = const()[name = string("x_263_pad_type_0"), val = string("valid")]; + tensor x_263_strides_0 = const()[name = string("x_263_strides_0"), val = tensor([1])]; + tensor x_263_pad_0 = const()[name = string("x_263_pad_0"), val = tensor([0, 0])]; + tensor x_263_dilations_0 = const()[name = string("x_263_dilations_0"), val = tensor([1])]; + int32 x_263_groups_0 = const()[name = string("x_263_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197588608))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197719744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198250496))))[name = string("module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_625_cast_fp16)[name = string("x_263_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = string("transpose_229")]; + tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = string("input_629_cast_fp16")]; + tensor input_631_axes_0 = const()[name = string("input_631_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198266944)))]; + tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198269056)))]; + tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198271168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198795520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200913152))))[name = string("module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_631_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200978752))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201503104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203612352))))[name = string("module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_635_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2160_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2159_to_fp16)[name = string("op_2160_cast_fp16")]; + tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2160_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor input_643_axes_0 = const()[name = string("input_643_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = string("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203628800)))]; + tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = string("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203630912)))]; + tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = string("input_643_cast_fp16")]; + tensor input_645_axes_0 = const()[name = string("input_645_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203633024)))]; + tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203635136)))]; + tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = string("input_645_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203637248))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204161600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206274752))))[name = string("module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_645_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206340352))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206864704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208973248))))[name = string("module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_649_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_2188_to_fp16 = const()[name = string("op_2188_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2189_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2188_to_fp16)[name = string("op_2189_cast_fp16")]; + tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2189_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208989696)))]; + tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208991808)))]; + tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = string("query_25_cast_fp16")]; + tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208993920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209125056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209653568))))[name = string("module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_2205 = const()[name = string("op_2205"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_2205, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209670016))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209801152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210329088))))[name = string("module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_2209 = const()[name = string("op_2209"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_2209, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210345536))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210476672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211004800))))[name = string("module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_2213 = const()[name = string("op_2213"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_2213, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211021248)))]; + tensor var_2225_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2225_cast_fp16")]; + tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211023360)))]; + tensor var_2227_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2227_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_271_transpose_x_0 = const()[name = string("x_271_transpose_x_0"), val = bool(false)]; + bool x_271_transpose_y_0 = const()[name = string("x_271_transpose_y_0"), val = bool(false)]; + tensor var_2229_to_fp16 = const()[name = string("op_2229_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211025472)))]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2227_cast_fp16)[name = string("transpose_228")]; + tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2229_to_fp16)[name = string("x_271_cast_fp16")]; + tensor x_273_pad_0 = const()[name = string("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_273_mode_0 = const()[name = string("x_273_mode_0"), val = string("constant")]; + fp16 const_134_to_fp16 = const()[name = string("const_134_to_fp16"), val = fp16(0x0p+0)]; + tensor x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2237 = const()[name = string("op_2237"), val = tensor([1, 8, -1, 188])]; + tensor x_275_cast_fp16 = reshape(shape = var_2237, x = x_273_cast_fp16)[name = string("x_275_cast_fp16")]; + tensor var_2241_begin_0 = const()[name = string("op_2241_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2241_end_0 = const()[name = string("op_2241_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2241_end_mask_0 = const()[name = string("op_2241_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2241_cast_fp16 = slice_by_index(begin = var_2241_begin_0, end = var_2241_end_0, end_mask = var_2241_end_mask_0, x = x_275_cast_fp16)[name = string("op_2241_cast_fp16")]; + tensor var_2242 = const()[name = string("op_2242"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2242, x = var_2241_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2225_cast_fp16)[name = string("transpose_227")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_2251_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2251_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_2251_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = string("scores_51_cast_fp16")]; + tensor var_2257_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = string("op_2257_cast_fp16")]; + tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2257_cast_fp16, cond = mask_3)[name = string("input_657_cast_fp16")]; + bool x_277_transpose_x_0 = const()[name = string("x_277_transpose_x_0"), val = bool(false)]; + bool x_277_transpose_y_0 = const()[name = string("x_277_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")]; + tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor var_2261_perm_0 = const()[name = string("op_2261_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2262 = const()[name = string("op_2262"), val = tensor([1, -1, 1024])]; + tensor var_2261_cast_fp16 = transpose(perm = var_2261_perm_0, x = x_277_cast_fp16)[name = string("transpose_224")]; + tensor input_659_cast_fp16 = reshape(shape = var_2262, x = var_2261_cast_fp16)[name = string("input_659_cast_fp16")]; + tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211793536))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211924672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212452288))))[name = string("module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_659_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = string("input_663_cast_fp16")]; + tensor x_281_axes_0 = const()[name = string("x_281_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212468736)))]; + tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212470848)))]; + tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_665_perm_0 = const()[name = string("input_665_perm_0"), val = tensor([0, 2, 1])]; + string input_667_pad_type_0 = const()[name = string("input_667_pad_type_0"), val = string("valid")]; + tensor input_667_strides_0 = const()[name = string("input_667_strides_0"), val = tensor([1])]; + tensor input_667_pad_0 = const()[name = string("input_667_pad_0"), val = tensor([0, 0])]; + tensor input_667_dilations_0 = const()[name = string("input_667_dilations_0"), val = tensor([1])]; + int32 input_667_groups_0 = const()[name = string("input_667_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212472960))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212735168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213790528))))[name = string("module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = string("transpose_223")]; + tensor input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_665_cast_fp16)[name = string("input_667_cast_fp16")]; + int32 x_283_split_num_splits_0 = const()[name = string("x_283_split_num_splits_0"), val = int32(2)]; + int32 x_283_split_axis_0 = const()[name = string("x_283_split_axis_0"), val = int32(1)]; + tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = string("x_283_split_cast_fp16")]; + tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = string("x_283_split_1_sigmoid_cast_fp16")]; + tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_328)[name = string("input_669_cast_fp16")]; + tensor input_671_pad_0 = const()[name = string("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_671_mode_0 = const()[name = string("input_671_mode_0"), val = string("constant")]; + fp16 const_137_to_fp16 = const()[name = string("const_137_to_fp16"), val = fp16(0x0p+0)]; + tensor input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = string("input_671_cast_fp16")]; + string input_673_pad_type_0 = const()[name = string("input_673_pad_type_0"), val = string("valid")]; + int32 input_673_groups_0 = const()[name = string("input_673_groups_0"), val = int32(1024)]; + tensor input_673_strides_0 = const()[name = string("input_673_strides_0"), val = tensor([1])]; + tensor input_673_pad_0 = const()[name = string("input_673_pad_0"), val = tensor([0, 0])]; + tensor input_673_dilations_0 = const()[name = string("input_673_dilations_0"), val = tensor([1])]; + tensor const_272_to_fp16 = const()[name = string("const_272_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213823360)))]; + tensor const_273_to_fp16 = const()[name = string("const_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213841856)))]; + tensor input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16, x = input_671_cast_fp16)[name = string("input_675_cast_fp16")]; + tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = string("input_677_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213843968))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213975104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214503488))))[name = string("module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_677_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = string("transpose_222")]; + tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = string("input_681_cast_fp16")]; + tensor input_683_axes_0 = const()[name = string("input_683_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214519936)))]; + tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214522048)))]; + tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214524160))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215048512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217161856))))[name = string("module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_683_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217227456))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217751808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219860160))))[name = string("module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_687_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_2322_to_fp16 = const()[name = string("op_2322_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2323_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2322_to_fp16)[name = string("op_2323_cast_fp16")]; + tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2323_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor input_695_axes_0 = const()[name = string("input_695_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = string("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219876608)))]; + tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = string("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219878720)))]; + tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = string("input_695_cast_fp16")]; + tensor input_697_axes_0 = const()[name = string("input_697_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219880832)))]; + tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219882944)))]; + tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = string("input_697_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219885056))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220409408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222523584))))[name = string("module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_697_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222589184))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223113536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225222336))))[name = string("module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_701_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_2351_to_fp16 = const()[name = string("op_2351_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2352_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2351_to_fp16)[name = string("op_2352_cast_fp16")]; + tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2352_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225238784)))]; + tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225240896)))]; + tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = string("query_27_cast_fp16")]; + tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225243008))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225374144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225901888))))[name = string("module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_2368 = const()[name = string("op_2368"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_2368, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225918336))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226049472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226577600))))[name = string("module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_2372 = const()[name = string("op_2372"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_2372, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226594048))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226725184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227253184))))[name = string("module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_2376 = const()[name = string("op_2376"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_2376, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227269632)))]; + tensor var_2388_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2388_cast_fp16")]; + tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227271744)))]; + tensor var_2390_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2390_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor var_2392_to_fp16 = const()[name = string("op_2392_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227273856)))]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2390_cast_fp16)[name = string("transpose_221")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2392_to_fp16)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2400 = const()[name = string("op_2400"), val = tensor([1, 8, -1, 188])]; + tensor x_297_cast_fp16 = reshape(shape = var_2400, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2404_begin_0 = const()[name = string("op_2404_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2404_end_0 = const()[name = string("op_2404_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2404_end_mask_0 = const()[name = string("op_2404_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2404_cast_fp16 = slice_by_index(begin = var_2404_begin_0, end = var_2404_end_0, end_mask = var_2404_end_mask_0, x = x_297_cast_fp16)[name = string("op_2404_cast_fp16")]; + tensor var_2405 = const()[name = string("op_2405"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2405, x = var_2404_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2388_cast_fp16)[name = string("transpose_220")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_2414_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2414_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_2414_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = string("scores_55_cast_fp16")]; + tensor var_2420_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = string("op_2420_cast_fp16")]; + tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2420_cast_fp16, cond = mask_3)[name = string("input_709_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2424_perm_0 = const()[name = string("op_2424_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2425 = const()[name = string("op_2425"), val = tensor([1, -1, 1024])]; + tensor var_2424_cast_fp16 = transpose(perm = var_2424_perm_0, x = x_299_cast_fp16)[name = string("transpose_217")]; + tensor input_711_cast_fp16 = reshape(shape = var_2425, x = var_2424_cast_fp16)[name = string("input_711_cast_fp16")]; + tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228041920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228173056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228701056))))[name = string("module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_711_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = string("input_715_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228717504)))]; + tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228719616)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_717_perm_0 = const()[name = string("input_717_perm_0"), val = tensor([0, 2, 1])]; + string input_719_pad_type_0 = const()[name = string("input_719_pad_type_0"), val = string("valid")]; + tensor input_719_strides_0 = const()[name = string("input_719_strides_0"), val = tensor([1])]; + tensor input_719_pad_0 = const()[name = string("input_719_pad_0"), val = tensor([0, 0])]; + tensor input_719_dilations_0 = const()[name = string("input_719_dilations_0"), val = tensor([1])]; + int32 input_719_groups_0 = const()[name = string("input_719_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228721728))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228983936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230039872))))[name = string("module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = string("transpose_216")]; + tensor input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_717_cast_fp16)[name = string("input_719_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_328)[name = string("input_721_cast_fp16")]; + tensor input_723_pad_0 = const()[name = string("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_723_mode_0 = const()[name = string("input_723_mode_0"), val = string("constant")]; + fp16 const_147_to_fp16 = const()[name = string("const_147_to_fp16"), val = fp16(0x0p+0)]; + tensor input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = string("input_723_cast_fp16")]; + string input_725_pad_type_0 = const()[name = string("input_725_pad_type_0"), val = string("valid")]; + int32 input_725_groups_0 = const()[name = string("input_725_groups_0"), val = int32(1024)]; + tensor input_725_strides_0 = const()[name = string("input_725_strides_0"), val = tensor([1])]; + tensor input_725_pad_0 = const()[name = string("input_725_pad_0"), val = tensor([0, 0])]; + tensor input_725_dilations_0 = const()[name = string("input_725_dilations_0"), val = tensor([1])]; + tensor const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230072704)))]; + tensor const_275_to_fp16 = const()[name = string("const_275_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230091200)))]; + tensor input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16, x = input_723_cast_fp16)[name = string("input_727_cast_fp16")]; + tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = string("input_729_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230093312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230224448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230753152))))[name = string("module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_729_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = string("transpose_215")]; + tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = string("input_733_cast_fp16")]; + tensor input_735_axes_0 = const()[name = string("input_735_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230769600)))]; + tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230771712)))]; + tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230773824))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231298176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233411264))))[name = string("module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_735_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233476864))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234001216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236109632))))[name = string("module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_739_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_2485_to_fp16 = const()[name = string("op_2485_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2486_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2485_to_fp16)[name = string("op_2486_cast_fp16")]; + tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2486_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor input_747_axes_0 = const()[name = string("input_747_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = string("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236126080)))]; + tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = string("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236128192)))]; + tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = string("input_747_cast_fp16")]; + tensor input_749_axes_0 = const()[name = string("input_749_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236130304)))]; + tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236132416)))]; + tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = string("input_749_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236134528))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236658880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238774144))))[name = string("module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_749_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238839744))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239364096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241471872))))[name = string("module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_753_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_2514_to_fp16 = const()[name = string("op_2514_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2515_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2514_to_fp16)[name = string("op_2515_cast_fp16")]; + tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2515_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241488320)))]; + tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241490432)))]; + tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = string("query_29_cast_fp16")]; + tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241492544))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241623680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242151040))))[name = string("module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_2531 = const()[name = string("op_2531"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_2531, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242167488))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242298624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242826688))))[name = string("module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_2535 = const()[name = string("op_2535"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_2535, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242843136))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242974272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243501312))))[name = string("module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_2539 = const()[name = string("op_2539"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_2539, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243517760)))]; + tensor var_2551_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2551_cast_fp16")]; + tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243519872)))]; + tensor var_2553_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2553_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_315_transpose_x_0 = const()[name = string("x_315_transpose_x_0"), val = bool(false)]; + bool x_315_transpose_y_0 = const()[name = string("x_315_transpose_y_0"), val = bool(false)]; + tensor var_2555_to_fp16 = const()[name = string("op_2555_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243521984)))]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2553_cast_fp16)[name = string("transpose_214")]; + tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2555_to_fp16)[name = string("x_315_cast_fp16")]; + tensor x_317_pad_0 = const()[name = string("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_317_mode_0 = const()[name = string("x_317_mode_0"), val = string("constant")]; + fp16 const_154_to_fp16 = const()[name = string("const_154_to_fp16"), val = fp16(0x0p+0)]; + tensor x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")]; + tensor var_2563 = const()[name = string("op_2563"), val = tensor([1, 8, -1, 188])]; + tensor x_319_cast_fp16 = reshape(shape = var_2563, x = x_317_cast_fp16)[name = string("x_319_cast_fp16")]; + tensor var_2567_begin_0 = const()[name = string("op_2567_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2567_end_0 = const()[name = string("op_2567_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2567_end_mask_0 = const()[name = string("op_2567_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2567_cast_fp16 = slice_by_index(begin = var_2567_begin_0, end = var_2567_end_0, end_mask = var_2567_end_mask_0, x = x_319_cast_fp16)[name = string("op_2567_cast_fp16")]; + tensor var_2568 = const()[name = string("op_2568"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2568, x = var_2567_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2551_cast_fp16)[name = string("transpose_213")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_2577_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_2577_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_2577_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = string("scores_59_cast_fp16")]; + tensor var_2583_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = string("op_2583_cast_fp16")]; + tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2583_cast_fp16, cond = mask_3)[name = string("input_761_cast_fp16")]; + bool x_321_transpose_x_0 = const()[name = string("x_321_transpose_x_0"), val = bool(false)]; + bool x_321_transpose_y_0 = const()[name = string("x_321_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")]; + tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_2587_perm_0 = const()[name = string("op_2587_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2588 = const()[name = string("op_2588"), val = tensor([1, -1, 1024])]; + tensor var_2587_cast_fp16 = transpose(perm = var_2587_perm_0, x = x_321_cast_fp16)[name = string("transpose_210")]; + tensor input_763_cast_fp16 = reshape(shape = var_2588, x = var_2587_cast_fp16)[name = string("input_763_cast_fp16")]; + tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244290048))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244421184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244948864))))[name = string("module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_763_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = string("input_767_cast_fp16")]; + tensor x_325_axes_0 = const()[name = string("x_325_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244965312)))]; + tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244967424)))]; + tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor input_769_perm_0 = const()[name = string("input_769_perm_0"), val = tensor([0, 2, 1])]; + string input_771_pad_type_0 = const()[name = string("input_771_pad_type_0"), val = string("valid")]; + tensor input_771_strides_0 = const()[name = string("input_771_strides_0"), val = tensor([1])]; + tensor input_771_pad_0 = const()[name = string("input_771_pad_0"), val = tensor([0, 0])]; + tensor input_771_dilations_0 = const()[name = string("input_771_dilations_0"), val = tensor([1])]; + int32 input_771_groups_0 = const()[name = string("input_771_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244969536))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245231744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246287168))))[name = string("module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = string("transpose_209")]; + tensor input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_769_cast_fp16)[name = string("input_771_cast_fp16")]; + int32 x_327_split_num_splits_0 = const()[name = string("x_327_split_num_splits_0"), val = int32(2)]; + int32 x_327_split_axis_0 = const()[name = string("x_327_split_axis_0"), val = int32(1)]; + tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = string("x_327_split_cast_fp16")]; + tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = string("x_327_split_1_sigmoid_cast_fp16")]; + tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = string("x_327_cast_fp16")]; + tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_328)[name = string("input_773_cast_fp16")]; + tensor input_775_pad_0 = const()[name = string("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_775_mode_0 = const()[name = string("input_775_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = string("input_775_cast_fp16")]; + string input_777_pad_type_0 = const()[name = string("input_777_pad_type_0"), val = string("valid")]; + int32 input_777_groups_0 = const()[name = string("input_777_groups_0"), val = int32(1024)]; + tensor input_777_strides_0 = const()[name = string("input_777_strides_0"), val = tensor([1])]; + tensor input_777_pad_0 = const()[name = string("input_777_pad_0"), val = tensor([0, 0])]; + tensor input_777_dilations_0 = const()[name = string("input_777_dilations_0"), val = tensor([1])]; + tensor const_276_to_fp16 = const()[name = string("const_276_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246320000)))]; + tensor const_277_to_fp16 = const()[name = string("const_277_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246338496)))]; + tensor input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16, x = input_775_cast_fp16)[name = string("input_779_cast_fp16")]; + tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = string("input_781_cast_fp16")]; + string x_329_pad_type_0 = const()[name = string("x_329_pad_type_0"), val = string("valid")]; + tensor x_329_strides_0 = const()[name = string("x_329_strides_0"), val = tensor([1])]; + tensor x_329_pad_0 = const()[name = string("x_329_pad_0"), val = tensor([0, 0])]; + tensor x_329_dilations_0 = const()[name = string("x_329_dilations_0"), val = tensor([1])]; + int32 x_329_groups_0 = const()[name = string("x_329_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246340608))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246471744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247001152))))[name = string("module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_781_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = string("transpose_208")]; + tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = string("input_785_cast_fp16")]; + tensor input_787_axes_0 = const()[name = string("input_787_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247017600)))]; + tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247019712)))]; + tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247021824))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247546176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249659968))))[name = string("module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_787_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249725568))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250249920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252358272))))[name = string("module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_791_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_2648_to_fp16 = const()[name = string("op_2648_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2649_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2648_to_fp16)[name = string("op_2649_cast_fp16")]; + tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2649_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor input_799_axes_0 = const()[name = string("input_799_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = string("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252374720)))]; + tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = string("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252376832)))]; + tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = string("input_799_cast_fp16")]; + tensor input_801_axes_0 = const()[name = string("input_801_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252378944)))]; + tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252381056)))]; + tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = string("input_801_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252383168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252907520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255022272))))[name = string("module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_801_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255087872))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255612224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257721408))))[name = string("module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_805_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_2677_to_fp16 = const()[name = string("op_2677_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2678_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2677_to_fp16)[name = string("op_2678_cast_fp16")]; + tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2678_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257737856)))]; + tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257739968)))]; + tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = string("query_31_cast_fp16")]; + tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257742080))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257873216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258401280))))[name = string("module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_2694 = const()[name = string("op_2694"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_2694, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258417728))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258548864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259076864))))[name = string("module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_2698 = const()[name = string("op_2698"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_2698, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259093312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259224448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259751808))))[name = string("module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_2702 = const()[name = string("op_2702"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_2702, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259768256)))]; + tensor var_2714_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_2714_cast_fp16")]; + tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259770368)))]; + tensor var_2716_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_2716_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_337_transpose_x_0 = const()[name = string("x_337_transpose_x_0"), val = bool(false)]; + bool x_337_transpose_y_0 = const()[name = string("x_337_transpose_y_0"), val = bool(false)]; + tensor var_2718_to_fp16 = const()[name = string("op_2718_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259772480)))]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2716_cast_fp16)[name = string("transpose_207")]; + tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_2718_to_fp16)[name = string("x_337_cast_fp16")]; + tensor x_339_pad_0 = const()[name = string("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_339_mode_0 = const()[name = string("x_339_mode_0"), val = string("constant")]; + fp16 const_164_to_fp16 = const()[name = string("const_164_to_fp16"), val = fp16(0x0p+0)]; + tensor x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")]; + tensor var_2726 = const()[name = string("op_2726"), val = tensor([1, 8, -1, 188])]; + tensor x_341_cast_fp16 = reshape(shape = var_2726, x = x_339_cast_fp16)[name = string("x_341_cast_fp16")]; + tensor var_2730_begin_0 = const()[name = string("op_2730_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2730_end_0 = const()[name = string("op_2730_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2730_end_mask_0 = const()[name = string("op_2730_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2730_cast_fp16 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_341_cast_fp16)[name = string("op_2730_cast_fp16")]; + tensor var_2731 = const()[name = string("op_2731"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_2731, x = var_2730_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2714_cast_fp16)[name = string("transpose_206")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_2740_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_2740_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_2740_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = string("scores_63_cast_fp16")]; + tensor var_2746_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = string("op_2746_cast_fp16")]; + tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2746_cast_fp16, cond = mask_3)[name = string("input_813_cast_fp16")]; + bool x_343_transpose_x_0 = const()[name = string("x_343_transpose_x_0"), val = bool(false)]; + bool x_343_transpose_y_0 = const()[name = string("x_343_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")]; + tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = string("x_343_cast_fp16")]; + tensor var_2750_perm_0 = const()[name = string("op_2750_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2751 = const()[name = string("op_2751"), val = tensor([1, -1, 1024])]; + tensor var_2750_cast_fp16 = transpose(perm = var_2750_perm_0, x = x_343_cast_fp16)[name = string("transpose_203")]; + tensor input_815_cast_fp16 = reshape(shape = var_2751, x = var_2750_cast_fp16)[name = string("input_815_cast_fp16")]; + tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260540544))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260671680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261199744))))[name = string("module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_815_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = string("input_819_cast_fp16")]; + tensor x_347_axes_0 = const()[name = string("x_347_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261216192)))]; + tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261218304)))]; + tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor input_821_perm_0 = const()[name = string("input_821_perm_0"), val = tensor([0, 2, 1])]; + string input_823_pad_type_0 = const()[name = string("input_823_pad_type_0"), val = string("valid")]; + tensor input_823_strides_0 = const()[name = string("input_823_strides_0"), val = tensor([1])]; + tensor input_823_pad_0 = const()[name = string("input_823_pad_0"), val = tensor([0, 0])]; + tensor input_823_dilations_0 = const()[name = string("input_823_dilations_0"), val = tensor([1])]; + int32 input_823_groups_0 = const()[name = string("input_823_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261220416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261482624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262537920))))[name = string("module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = string("transpose_202")]; + tensor input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_821_cast_fp16)[name = string("input_823_cast_fp16")]; + int32 x_349_split_num_splits_0 = const()[name = string("x_349_split_num_splits_0"), val = int32(2)]; + int32 x_349_split_axis_0 = const()[name = string("x_349_split_axis_0"), val = int32(1)]; + tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = string("x_349_split_cast_fp16")]; + tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = string("x_349_split_1_sigmoid_cast_fp16")]; + tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_328)[name = string("input_825_cast_fp16")]; + tensor input_827_pad_0 = const()[name = string("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_827_mode_0 = const()[name = string("input_827_mode_0"), val = string("constant")]; + fp16 const_167_to_fp16 = const()[name = string("const_167_to_fp16"), val = fp16(0x0p+0)]; + tensor input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = string("input_827_cast_fp16")]; + string input_829_pad_type_0 = const()[name = string("input_829_pad_type_0"), val = string("valid")]; + int32 input_829_groups_0 = const()[name = string("input_829_groups_0"), val = int32(1024)]; + tensor input_829_strides_0 = const()[name = string("input_829_strides_0"), val = tensor([1])]; + tensor input_829_pad_0 = const()[name = string("input_829_pad_0"), val = tensor([0, 0])]; + tensor input_829_dilations_0 = const()[name = string("input_829_dilations_0"), val = tensor([1])]; + tensor const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262570752)))]; + tensor const_279_to_fp16 = const()[name = string("const_279_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262589248)))]; + tensor input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16, x = input_827_cast_fp16)[name = string("input_831_cast_fp16")]; + tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = string("input_833_cast_fp16")]; + string x_351_pad_type_0 = const()[name = string("x_351_pad_type_0"), val = string("valid")]; + tensor x_351_strides_0 = const()[name = string("x_351_strides_0"), val = tensor([1])]; + tensor x_351_pad_0 = const()[name = string("x_351_pad_0"), val = tensor([0, 0])]; + tensor x_351_dilations_0 = const()[name = string("x_351_dilations_0"), val = tensor([1])]; + int32 x_351_groups_0 = const()[name = string("x_351_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262591360))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262722496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263252224))))[name = string("module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_833_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = string("transpose_201")]; + tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = string("input_837_cast_fp16")]; + tensor input_839_axes_0 = const()[name = string("input_839_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263268672)))]; + tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263270784)))]; + tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263272896))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263797248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265911936))))[name = string("module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_839_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265977536))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266501888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268610624))))[name = string("module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_843_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_2811_to_fp16 = const()[name = string("op_2811_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2812_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2811_to_fp16)[name = string("op_2812_cast_fp16")]; + tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2812_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor input_851_axes_0 = const()[name = string("input_851_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = string("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268627072)))]; + tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = string("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268629184)))]; + tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = string("input_851_cast_fp16")]; + tensor input_853_axes_0 = const()[name = string("input_853_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268631296)))]; + tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268633408)))]; + tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = string("input_853_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268635520))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269159872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271275328))))[name = string("module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_853_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271340928))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271865280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273973120))))[name = string("module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_857_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_2840_to_fp16 = const()[name = string("op_2840_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2841_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2840_to_fp16)[name = string("op_2841_cast_fp16")]; + tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2841_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273989568)))]; + tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273991680)))]; + tensor query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = string("query_33_cast_fp16")]; + tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273993792))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274124928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274651968))))[name = string("module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_2857 = const()[name = string("op_2857"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_2857, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274668416))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274799552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275327360))))[name = string("module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_2861 = const()[name = string("op_2861"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_2861, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275343808))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275474944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276001920))))[name = string("module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_2865 = const()[name = string("op_2865"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_2865, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276018368)))]; + tensor var_2877_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_2877_cast_fp16")]; + tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276020480)))]; + tensor var_2879_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_2879_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_359_transpose_x_0 = const()[name = string("x_359_transpose_x_0"), val = bool(false)]; + bool x_359_transpose_y_0 = const()[name = string("x_359_transpose_y_0"), val = bool(false)]; + tensor var_2881_to_fp16 = const()[name = string("op_2881_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276022592)))]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2879_cast_fp16)[name = string("transpose_200")]; + tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = var_2881_to_fp16)[name = string("x_359_cast_fp16")]; + tensor x_361_pad_0 = const()[name = string("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_361_mode_0 = const()[name = string("x_361_mode_0"), val = string("constant")]; + fp16 const_174_to_fp16 = const()[name = string("const_174_to_fp16"), val = fp16(0x0p+0)]; + tensor x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor var_2889 = const()[name = string("op_2889"), val = tensor([1, 8, -1, 188])]; + tensor x_363_cast_fp16 = reshape(shape = var_2889, x = x_361_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor var_2893_begin_0 = const()[name = string("op_2893_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2893_end_0 = const()[name = string("op_2893_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2893_end_mask_0 = const()[name = string("op_2893_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2893_cast_fp16 = slice_by_index(begin = var_2893_begin_0, end = var_2893_end_0, end_mask = var_2893_end_mask_0, x = x_363_cast_fp16)[name = string("op_2893_cast_fp16")]; + tensor var_2894 = const()[name = string("op_2894"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_2894, x = var_2893_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2877_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_2903_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_2903_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_2903_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = string("scores_67_cast_fp16")]; + tensor var_2909_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = string("op_2909_cast_fp16")]; + tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_2909_cast_fp16, cond = mask_3)[name = string("input_865_cast_fp16")]; + bool x_365_transpose_x_0 = const()[name = string("x_365_transpose_x_0"), val = bool(false)]; + bool x_365_transpose_y_0 = const()[name = string("x_365_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")]; + tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_35_cast_fp16)[name = string("x_365_cast_fp16")]; + tensor var_2913_perm_0 = const()[name = string("op_2913_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2914 = const()[name = string("op_2914"), val = tensor([1, -1, 1024])]; + tensor var_2913_cast_fp16 = transpose(perm = var_2913_perm_0, x = x_365_cast_fp16)[name = string("transpose_196")]; + tensor input_867_cast_fp16 = reshape(shape = var_2914, x = var_2913_cast_fp16)[name = string("input_867_cast_fp16")]; + tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276790656))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276921792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277450048))))[name = string("module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_867_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = string("input_871_cast_fp16")]; + tensor x_369_axes_0 = const()[name = string("x_369_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277466496)))]; + tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277468608)))]; + tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = string("x_369_cast_fp16")]; + tensor input_873_perm_0 = const()[name = string("input_873_perm_0"), val = tensor([0, 2, 1])]; + string input_875_pad_type_0 = const()[name = string("input_875_pad_type_0"), val = string("valid")]; + tensor input_875_strides_0 = const()[name = string("input_875_strides_0"), val = tensor([1])]; + tensor input_875_pad_0 = const()[name = string("input_875_pad_0"), val = tensor([0, 0])]; + tensor input_875_dilations_0 = const()[name = string("input_875_dilations_0"), val = tensor([1])]; + int32 input_875_groups_0 = const()[name = string("input_875_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277470720))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277732928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278786816))))[name = string("module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = string("transpose_195")]; + tensor input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_873_cast_fp16)[name = string("input_875_cast_fp16")]; + int32 x_371_split_num_splits_0 = const()[name = string("x_371_split_num_splits_0"), val = int32(2)]; + int32 x_371_split_axis_0 = const()[name = string("x_371_split_axis_0"), val = int32(1)]; + tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = string("x_371_split_cast_fp16")]; + tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = string("x_371_split_1_sigmoid_cast_fp16")]; + tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = string("x_371_cast_fp16")]; + tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_328)[name = string("input_877_cast_fp16")]; + tensor input_879_pad_0 = const()[name = string("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_879_mode_0 = const()[name = string("input_879_mode_0"), val = string("constant")]; + fp16 const_177_to_fp16 = const()[name = string("const_177_to_fp16"), val = fp16(0x0p+0)]; + tensor input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = string("input_879_cast_fp16")]; + string input_881_pad_type_0 = const()[name = string("input_881_pad_type_0"), val = string("valid")]; + int32 input_881_groups_0 = const()[name = string("input_881_groups_0"), val = int32(1024)]; + tensor input_881_strides_0 = const()[name = string("input_881_strides_0"), val = tensor([1])]; + tensor input_881_pad_0 = const()[name = string("input_881_pad_0"), val = tensor([0, 0])]; + tensor input_881_dilations_0 = const()[name = string("input_881_dilations_0"), val = tensor([1])]; + tensor const_280_to_fp16 = const()[name = string("const_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278819648)))]; + tensor const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278838144)))]; + tensor input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16, x = input_879_cast_fp16)[name = string("input_883_cast_fp16")]; + tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = string("input_885_cast_fp16")]; + string x_373_pad_type_0 = const()[name = string("x_373_pad_type_0"), val = string("valid")]; + tensor x_373_strides_0 = const()[name = string("x_373_strides_0"), val = tensor([1])]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0])]; + tensor x_373_dilations_0 = const()[name = string("x_373_dilations_0"), val = tensor([1])]; + int32 x_373_groups_0 = const()[name = string("x_373_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278840256))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278971392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279500544))))[name = string("module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_885_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = string("transpose_194")]; + tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = string("input_889_cast_fp16")]; + tensor input_891_axes_0 = const()[name = string("input_891_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279516992)))]; + tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279519104)))]; + tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279521216))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280045568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282157504))))[name = string("module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_891_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282223104))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282747456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284856512))))[name = string("module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_895_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_2974_to_fp16 = const()[name = string("op_2974_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2975_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2974_to_fp16)[name = string("op_2975_cast_fp16")]; + tensor input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2975_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor input_903_axes_0 = const()[name = string("input_903_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = string("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284872960)))]; + tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = string("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284875072)))]; + tensor input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = string("input_903_cast_fp16")]; + tensor input_905_axes_0 = const()[name = string("input_905_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284877184)))]; + tensor module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284879296)))]; + tensor input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = string("input_905_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284881408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285405760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287519808))))[name = string("module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_905_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287585408))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288109760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290218304))))[name = string("module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_909_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_3003_to_fp16 = const()[name = string("op_3003_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3004_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3003_to_fp16)[name = string("op_3004_cast_fp16")]; + tensor input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3004_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290234752)))]; + tensor module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290236864)))]; + tensor query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = string("query_35_cast_fp16")]; + tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290238976))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290370112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290897792))))[name = string("module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_3020 = const()[name = string("op_3020"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_3020, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290914240))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291045376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291572992))))[name = string("module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_3024 = const()[name = string("op_3024"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_3024, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291589440))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291720576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292247552))))[name = string("module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_3028 = const()[name = string("op_3028"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_3028, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292264000)))]; + tensor var_3040_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3040_cast_fp16")]; + tensor module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292266112)))]; + tensor var_3042_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3042_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_381_transpose_x_0 = const()[name = string("x_381_transpose_x_0"), val = bool(false)]; + bool x_381_transpose_y_0 = const()[name = string("x_381_transpose_y_0"), val = bool(false)]; + tensor var_3044_to_fp16 = const()[name = string("op_3044_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292268224)))]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3042_cast_fp16)[name = string("transpose_193")]; + tensor x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = var_3044_to_fp16)[name = string("x_381_cast_fp16")]; + tensor x_383_pad_0 = const()[name = string("x_383_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_383_mode_0 = const()[name = string("x_383_mode_0"), val = string("constant")]; + fp16 const_184_to_fp16 = const()[name = string("const_184_to_fp16"), val = fp16(0x0p+0)]; + tensor x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor var_3052 = const()[name = string("op_3052"), val = tensor([1, 8, -1, 188])]; + tensor x_385_cast_fp16 = reshape(shape = var_3052, x = x_383_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor var_3056_begin_0 = const()[name = string("op_3056_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3056_end_0 = const()[name = string("op_3056_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3056_end_mask_0 = const()[name = string("op_3056_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3056_cast_fp16 = slice_by_index(begin = var_3056_begin_0, end = var_3056_end_0, end_mask = var_3056_end_mask_0, x = x_385_cast_fp16)[name = string("op_3056_cast_fp16")]; + tensor var_3057 = const()[name = string("op_3057"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3057, x = var_3056_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3040_cast_fp16)[name = string("transpose_192")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_3066_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3066_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_3066_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = string("scores_71_cast_fp16")]; + tensor var_3072_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = string("op_3072_cast_fp16")]; + tensor input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3072_cast_fp16, cond = mask_3)[name = string("input_917_cast_fp16")]; + bool x_387_transpose_x_0 = const()[name = string("x_387_transpose_x_0"), val = bool(false)]; + bool x_387_transpose_y_0 = const()[name = string("x_387_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")]; + tensor x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_37_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor var_3076_perm_0 = const()[name = string("op_3076_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3077 = const()[name = string("op_3077"), val = tensor([1, -1, 1024])]; + tensor var_3076_cast_fp16 = transpose(perm = var_3076_perm_0, x = x_387_cast_fp16)[name = string("transpose_189")]; + tensor input_919_cast_fp16 = reshape(shape = var_3077, x = var_3076_cast_fp16)[name = string("input_919_cast_fp16")]; + tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293036288))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293167424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293694720))))[name = string("module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_919_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = string("input_923_cast_fp16")]; + tensor x_391_axes_0 = const()[name = string("x_391_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("module_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293711168)))]; + tensor module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("module_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293713280)))]; + tensor x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = string("x_391_cast_fp16")]; + tensor input_925_perm_0 = const()[name = string("input_925_perm_0"), val = tensor([0, 2, 1])]; + string input_927_pad_type_0 = const()[name = string("input_927_pad_type_0"), val = string("valid")]; + tensor input_927_strides_0 = const()[name = string("input_927_strides_0"), val = tensor([1])]; + tensor input_927_pad_0 = const()[name = string("input_927_pad_0"), val = tensor([0, 0])]; + tensor input_927_dilations_0 = const()[name = string("input_927_dilations_0"), val = tensor([1])]; + int32 input_927_groups_0 = const()[name = string("input_927_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293715392))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293977600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295031296))))[name = string("module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = string("transpose_188")]; + tensor input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_925_cast_fp16)[name = string("input_927_cast_fp16")]; + int32 x_393_split_num_splits_0 = const()[name = string("x_393_split_num_splits_0"), val = int32(2)]; + int32 x_393_split_axis_0 = const()[name = string("x_393_split_axis_0"), val = int32(1)]; + tensor x_393_split_cast_fp16_0, tensor x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = string("x_393_split_cast_fp16")]; + tensor x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = string("x_393_split_1_sigmoid_cast_fp16")]; + tensor x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = string("x_393_cast_fp16")]; + tensor input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_328)[name = string("input_929_cast_fp16")]; + tensor input_931_pad_0 = const()[name = string("input_931_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_931_mode_0 = const()[name = string("input_931_mode_0"), val = string("constant")]; + fp16 const_187_to_fp16 = const()[name = string("const_187_to_fp16"), val = fp16(0x0p+0)]; + tensor input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = string("input_931_cast_fp16")]; + string input_933_pad_type_0 = const()[name = string("input_933_pad_type_0"), val = string("valid")]; + int32 input_933_groups_0 = const()[name = string("input_933_groups_0"), val = int32(1024)]; + tensor input_933_strides_0 = const()[name = string("input_933_strides_0"), val = tensor([1])]; + tensor input_933_pad_0 = const()[name = string("input_933_pad_0"), val = tensor([0, 0])]; + tensor input_933_dilations_0 = const()[name = string("input_933_dilations_0"), val = tensor([1])]; + tensor const_282_to_fp16 = const()[name = string("const_282_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295064128)))]; + tensor const_283_to_fp16 = const()[name = string("const_283_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295082624)))]; + tensor input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16, x = input_931_cast_fp16)[name = string("input_935_cast_fp16")]; + tensor input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = string("input_937_cast_fp16")]; + string x_395_pad_type_0 = const()[name = string("x_395_pad_type_0"), val = string("valid")]; + tensor x_395_strides_0 = const()[name = string("x_395_strides_0"), val = tensor([1])]; + tensor x_395_pad_0 = const()[name = string("x_395_pad_0"), val = tensor([0, 0])]; + tensor x_395_dilations_0 = const()[name = string("x_395_dilations_0"), val = tensor([1])]; + int32 x_395_groups_0 = const()[name = string("x_395_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295084736))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295215872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295745216))))[name = string("module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_937_cast_fp16)[name = string("x_395_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = string("transpose_187")]; + tensor input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = string("input_941_cast_fp16")]; + tensor input_943_axes_0 = const()[name = string("input_943_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295761664)))]; + tensor module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295763776)))]; + tensor input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295765888))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296290240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298402176))))[name = string("module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_943_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298467776))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298992128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301101824))))[name = string("module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_947_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_3137_to_fp16 = const()[name = string("op_3137_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3138_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3137_to_fp16)[name = string("op_3138_cast_fp16")]; + tensor input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3138_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor input_955_axes_0 = const()[name = string("input_955_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_out_weight_to_fp16 = const()[name = string("module_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301118272)))]; + tensor module_layers_17_norm_out_bias_to_fp16 = const()[name = string("module_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301120384)))]; + tensor input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = string("input_955_cast_fp16")]; + tensor input_957_axes_0 = const()[name = string("input_957_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301122496)))]; + tensor module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301124608)))]; + tensor input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = string("input_957_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301126720))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301651072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303763392))))[name = string("module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_957_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303828992))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304353344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306461248))))[name = string("module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_961_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3167_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3166_to_fp16)[name = string("op_3167_cast_fp16")]; + tensor input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3167_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306477696)))]; + tensor module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306479808)))]; + tensor query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = string("query_37_cast_fp16")]; + tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306481920))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306613056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307140864))))[name = string("module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_3183 = const()[name = string("op_3183"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_3183, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307157312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307288448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307816192))))[name = string("module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_3187 = const()[name = string("op_3187"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_3187, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307832640))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307963776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308491520))))[name = string("module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_3191 = const()[name = string("op_3191"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_3191, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308507968)))]; + tensor var_3203_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3203_cast_fp16")]; + tensor module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308510080)))]; + tensor var_3205_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3205_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor var_3207_to_fp16 = const()[name = string("op_3207_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308512192)))]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3205_cast_fp16)[name = string("transpose_186")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = var_3207_to_fp16)[name = string("x_403_cast_fp16")]; + tensor x_405_pad_0 = const()[name = string("x_405_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_405_mode_0 = const()[name = string("x_405_mode_0"), val = string("constant")]; + fp16 const_194_to_fp16 = const()[name = string("const_194_to_fp16"), val = fp16(0x0p+0)]; + tensor x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")]; + tensor var_3215 = const()[name = string("op_3215"), val = tensor([1, 8, -1, 188])]; + tensor x_407_cast_fp16 = reshape(shape = var_3215, x = x_405_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor var_3219_begin_0 = const()[name = string("op_3219_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3219_end_0 = const()[name = string("op_3219_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3219_end_mask_0 = const()[name = string("op_3219_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3219_cast_fp16 = slice_by_index(begin = var_3219_begin_0, end = var_3219_end_0, end_mask = var_3219_end_mask_0, x = x_407_cast_fp16)[name = string("op_3219_cast_fp16")]; + tensor var_3220 = const()[name = string("op_3220"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_3220, x = var_3219_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3203_cast_fp16)[name = string("transpose_185")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_3229_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3229_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_3229_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = string("scores_75_cast_fp16")]; + tensor var_3235_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = string("op_3235_cast_fp16")]; + tensor input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3235_cast_fp16, cond = mask_3)[name = string("input_969_cast_fp16")]; + bool x_409_transpose_x_0 = const()[name = string("x_409_transpose_x_0"), val = bool(false)]; + bool x_409_transpose_y_0 = const()[name = string("x_409_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")]; + tensor x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_39_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor var_3239_perm_0 = const()[name = string("op_3239_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3240 = const()[name = string("op_3240"), val = tensor([1, -1, 1024])]; + tensor var_3239_cast_fp16 = transpose(perm = var_3239_perm_0, x = x_409_cast_fp16)[name = string("transpose_182")]; + tensor input_971_cast_fp16 = reshape(shape = var_3240, x = var_3239_cast_fp16)[name = string("input_971_cast_fp16")]; + tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309280256))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309411392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309939008))))[name = string("module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_971_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = string("input_975_cast_fp16")]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("module_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309955456)))]; + tensor module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("module_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309957568)))]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_977_perm_0 = const()[name = string("input_977_perm_0"), val = tensor([0, 2, 1])]; + string input_979_pad_type_0 = const()[name = string("input_979_pad_type_0"), val = string("valid")]; + tensor input_979_strides_0 = const()[name = string("input_979_strides_0"), val = tensor([1])]; + tensor input_979_pad_0 = const()[name = string("input_979_pad_0"), val = tensor([0, 0])]; + tensor input_979_dilations_0 = const()[name = string("input_979_dilations_0"), val = tensor([1])]; + int32 input_979_groups_0 = const()[name = string("input_979_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309959680))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310221888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311276416))))[name = string("module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = string("transpose_181")]; + tensor input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_977_cast_fp16)[name = string("input_979_cast_fp16")]; + int32 x_415_split_num_splits_0 = const()[name = string("x_415_split_num_splits_0"), val = int32(2)]; + int32 x_415_split_axis_0 = const()[name = string("x_415_split_axis_0"), val = int32(1)]; + tensor x_415_split_cast_fp16_0, tensor x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = string("x_415_split_cast_fp16")]; + tensor x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = string("x_415_split_1_sigmoid_cast_fp16")]; + tensor x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_328)[name = string("input_981_cast_fp16")]; + tensor input_983_pad_0 = const()[name = string("input_983_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_983_mode_0 = const()[name = string("input_983_mode_0"), val = string("constant")]; + fp16 const_197_to_fp16 = const()[name = string("const_197_to_fp16"), val = fp16(0x0p+0)]; + tensor input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = string("input_983_cast_fp16")]; + string input_985_pad_type_0 = const()[name = string("input_985_pad_type_0"), val = string("valid")]; + int32 input_985_groups_0 = const()[name = string("input_985_groups_0"), val = int32(1024)]; + tensor input_985_strides_0 = const()[name = string("input_985_strides_0"), val = tensor([1])]; + tensor input_985_pad_0 = const()[name = string("input_985_pad_0"), val = tensor([0, 0])]; + tensor input_985_dilations_0 = const()[name = string("input_985_dilations_0"), val = tensor([1])]; + tensor const_284_to_fp16 = const()[name = string("const_284_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311309248)))]; + tensor const_285_to_fp16 = const()[name = string("const_285_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311327744)))]; + tensor input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16, x = input_983_cast_fp16)[name = string("input_987_cast_fp16")]; + tensor input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = string("input_989_cast_fp16")]; + string x_417_pad_type_0 = const()[name = string("x_417_pad_type_0"), val = string("valid")]; + tensor x_417_strides_0 = const()[name = string("x_417_strides_0"), val = tensor([1])]; + tensor x_417_pad_0 = const()[name = string("x_417_pad_0"), val = tensor([0, 0])]; + tensor x_417_dilations_0 = const()[name = string("x_417_dilations_0"), val = tensor([1])]; + int32 x_417_groups_0 = const()[name = string("x_417_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311329856))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311460992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311989440))))[name = string("module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_989_cast_fp16)[name = string("x_417_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = string("transpose_180")]; + tensor input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = string("input_993_cast_fp16")]; + tensor input_995_axes_0 = const()[name = string("input_995_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312005888)))]; + tensor module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312008000)))]; + tensor input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312010112))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312534464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314645952))))[name = string("module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_995_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314711552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315235904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317343808))))[name = string("module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_999_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_3300_to_fp16 = const()[name = string("op_3300_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3301_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3300_to_fp16)[name = string("op_3301_cast_fp16")]; + tensor input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3301_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor input_1007_axes_0 = const()[name = string("input_1007_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_out_weight_to_fp16 = const()[name = string("module_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317360256)))]; + tensor module_layers_18_norm_out_bias_to_fp16 = const()[name = string("module_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317362368)))]; + tensor input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = string("input_1007_cast_fp16")]; + tensor input_1009_axes_0 = const()[name = string("input_1009_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317364480)))]; + tensor module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317366592)))]; + tensor input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = string("input_1009_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317368704))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317893056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320005824))))[name = string("module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1009_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320071424))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320595776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322703680))))[name = string("module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1013_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_3329_to_fp16 = const()[name = string("op_3329_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3330_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3329_to_fp16)[name = string("op_3330_cast_fp16")]; + tensor input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3330_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322720128)))]; + tensor module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322722240)))]; + tensor query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = string("query_39_cast_fp16")]; + tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322724352))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322855488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323382976))))[name = string("module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_3346 = const()[name = string("op_3346"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_3346, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323399424))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323530560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324058240))))[name = string("module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_3350 = const()[name = string("op_3350"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_3350, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324074688))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324205824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324732992))))[name = string("module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_3354 = const()[name = string("op_3354"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_3354, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324749440)))]; + tensor var_3366_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3366_cast_fp16")]; + tensor module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324751552)))]; + tensor var_3368_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3368_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_425_transpose_x_0 = const()[name = string("x_425_transpose_x_0"), val = bool(false)]; + bool x_425_transpose_y_0 = const()[name = string("x_425_transpose_y_0"), val = bool(false)]; + tensor var_3370_to_fp16 = const()[name = string("op_3370_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324753664)))]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3368_cast_fp16)[name = string("transpose_179")]; + tensor x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = var_3370_to_fp16)[name = string("x_425_cast_fp16")]; + tensor x_427_pad_0 = const()[name = string("x_427_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_427_mode_0 = const()[name = string("x_427_mode_0"), val = string("constant")]; + fp16 const_204_to_fp16 = const()[name = string("const_204_to_fp16"), val = fp16(0x0p+0)]; + tensor x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3378 = const()[name = string("op_3378"), val = tensor([1, 8, -1, 188])]; + tensor x_429_cast_fp16 = reshape(shape = var_3378, x = x_427_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3382_begin_0 = const()[name = string("op_3382_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3382_end_0 = const()[name = string("op_3382_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3382_end_mask_0 = const()[name = string("op_3382_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3382_cast_fp16 = slice_by_index(begin = var_3382_begin_0, end = var_3382_end_0, end_mask = var_3382_end_mask_0, x = x_429_cast_fp16)[name = string("op_3382_cast_fp16")]; + tensor var_3383 = const()[name = string("op_3383"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3383, x = var_3382_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3366_cast_fp16)[name = string("transpose_178")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_3392_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3392_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_3392_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = string("scores_79_cast_fp16")]; + tensor var_3398_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = string("op_3398_cast_fp16")]; + tensor input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3398_cast_fp16, cond = mask_3)[name = string("input_1021_cast_fp16")]; + bool x_431_transpose_x_0 = const()[name = string("x_431_transpose_x_0"), val = bool(false)]; + bool x_431_transpose_y_0 = const()[name = string("x_431_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")]; + tensor x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_41_cast_fp16)[name = string("x_431_cast_fp16")]; + tensor var_3402_perm_0 = const()[name = string("op_3402_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3403 = const()[name = string("op_3403"), val = tensor([1, -1, 1024])]; + tensor var_3402_cast_fp16 = transpose(perm = var_3402_perm_0, x = x_431_cast_fp16)[name = string("transpose_175")]; + tensor input_1023_cast_fp16 = reshape(shape = var_3403, x = var_3402_cast_fp16)[name = string("input_1023_cast_fp16")]; + tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325521728))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325652864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326181376))))[name = string("module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1023_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1027_cast_fp16")]; + tensor x_435_axes_0 = const()[name = string("x_435_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("module_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326197824)))]; + tensor module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("module_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326199936)))]; + tensor x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_1029_perm_0 = const()[name = string("input_1029_perm_0"), val = tensor([0, 2, 1])]; + string input_1031_pad_type_0 = const()[name = string("input_1031_pad_type_0"), val = string("valid")]; + tensor input_1031_strides_0 = const()[name = string("input_1031_strides_0"), val = tensor([1])]; + tensor input_1031_pad_0 = const()[name = string("input_1031_pad_0"), val = tensor([0, 0])]; + tensor input_1031_dilations_0 = const()[name = string("input_1031_dilations_0"), val = tensor([1])]; + int32 input_1031_groups_0 = const()[name = string("input_1031_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326202048))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326464256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327518336))))[name = string("module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = string("transpose_174")]; + tensor input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1029_cast_fp16)[name = string("input_1031_cast_fp16")]; + int32 x_437_split_num_splits_0 = const()[name = string("x_437_split_num_splits_0"), val = int32(2)]; + int32 x_437_split_axis_0 = const()[name = string("x_437_split_axis_0"), val = int32(1)]; + tensor x_437_split_cast_fp16_0, tensor x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = string("x_437_split_cast_fp16")]; + tensor x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = string("x_437_split_1_sigmoid_cast_fp16")]; + tensor x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_328)[name = string("input_1033_cast_fp16")]; + tensor input_1035_pad_0 = const()[name = string("input_1035_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1035_mode_0 = const()[name = string("input_1035_mode_0"), val = string("constant")]; + fp16 const_207_to_fp16 = const()[name = string("const_207_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = string("input_1035_cast_fp16")]; + string input_1037_pad_type_0 = const()[name = string("input_1037_pad_type_0"), val = string("valid")]; + int32 input_1037_groups_0 = const()[name = string("input_1037_groups_0"), val = int32(1024)]; + tensor input_1037_strides_0 = const()[name = string("input_1037_strides_0"), val = tensor([1])]; + tensor input_1037_pad_0 = const()[name = string("input_1037_pad_0"), val = tensor([0, 0])]; + tensor input_1037_dilations_0 = const()[name = string("input_1037_dilations_0"), val = tensor([1])]; + tensor const_286_to_fp16 = const()[name = string("const_286_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327551168)))]; + tensor const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327569664)))]; + tensor input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16, x = input_1035_cast_fp16)[name = string("input_1039_cast_fp16")]; + tensor input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = string("input_1041_cast_fp16")]; + string x_439_pad_type_0 = const()[name = string("x_439_pad_type_0"), val = string("valid")]; + tensor x_439_strides_0 = const()[name = string("x_439_strides_0"), val = tensor([1])]; + tensor x_439_pad_0 = const()[name = string("x_439_pad_0"), val = tensor([0, 0])]; + tensor x_439_dilations_0 = const()[name = string("x_439_dilations_0"), val = tensor([1])]; + int32 x_439_groups_0 = const()[name = string("x_439_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327571776))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327702912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328231104))))[name = string("module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1041_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = string("transpose_173")]; + tensor input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = string("input_1045_cast_fp16")]; + tensor input_1047_axes_0 = const()[name = string("input_1047_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328247552)))]; + tensor module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328249664)))]; + tensor input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328251776))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328776128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330886784))))[name = string("module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1047_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330952384))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331476736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333584576))))[name = string("module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1051_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_3463_to_fp16 = const()[name = string("op_3463_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3464_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3463_to_fp16)[name = string("op_3464_cast_fp16")]; + tensor input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3464_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor input_1059_axes_0 = const()[name = string("input_1059_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_out_weight_to_fp16 = const()[name = string("module_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333601024)))]; + tensor module_layers_19_norm_out_bias_to_fp16 = const()[name = string("module_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333603136)))]; + tensor input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = string("input_1059_cast_fp16")]; + tensor input_1061_axes_0 = const()[name = string("input_1061_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333605248)))]; + tensor module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333607360)))]; + tensor input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = string("input_1061_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333609472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334133824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336248512))))[name = string("module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1061_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336314112))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336838464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338946496))))[name = string("module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1065_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_3492_to_fp16 = const()[name = string("op_3492_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3493_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3492_to_fp16)[name = string("op_3493_cast_fp16")]; + tensor input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3493_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338962944)))]; + tensor module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338965056)))]; + tensor query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = string("query_41_cast_fp16")]; + tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338967168))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339098304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339626240))))[name = string("module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_3509 = const()[name = string("op_3509"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_3509, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339642688))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339773824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340301952))))[name = string("module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_3513 = const()[name = string("op_3513"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_3513, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340318400))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340449536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340976064))))[name = string("module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_3517 = const()[name = string("op_3517"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_3517, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340992512)))]; + tensor var_3529_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_3529_cast_fp16")]; + tensor module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340994624)))]; + tensor var_3531_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_3531_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_447_transpose_x_0 = const()[name = string("x_447_transpose_x_0"), val = bool(false)]; + bool x_447_transpose_y_0 = const()[name = string("x_447_transpose_y_0"), val = bool(false)]; + tensor var_3533_to_fp16 = const()[name = string("op_3533_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340996736)))]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3531_cast_fp16)[name = string("transpose_172")]; + tensor x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = var_3533_to_fp16)[name = string("x_447_cast_fp16")]; + tensor x_449_pad_0 = const()[name = string("x_449_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_449_mode_0 = const()[name = string("x_449_mode_0"), val = string("constant")]; + fp16 const_214_to_fp16 = const()[name = string("const_214_to_fp16"), val = fp16(0x0p+0)]; + tensor x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")]; + tensor var_3541 = const()[name = string("op_3541"), val = tensor([1, 8, -1, 188])]; + tensor x_451_cast_fp16 = reshape(shape = var_3541, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_3545_begin_0 = const()[name = string("op_3545_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3545_end_0 = const()[name = string("op_3545_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3545_end_mask_0 = const()[name = string("op_3545_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3545_cast_fp16 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = x_451_cast_fp16)[name = string("op_3545_cast_fp16")]; + tensor var_3546 = const()[name = string("op_3546"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_3546, x = var_3545_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3529_cast_fp16)[name = string("transpose_171")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_3555_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_3555_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_3555_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = string("scores_83_cast_fp16")]; + tensor var_3561_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = string("op_3561_cast_fp16")]; + tensor input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3561_cast_fp16, cond = mask_3)[name = string("input_1073_cast_fp16")]; + bool x_453_transpose_x_0 = const()[name = string("x_453_transpose_x_0"), val = bool(false)]; + bool x_453_transpose_y_0 = const()[name = string("x_453_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")]; + tensor x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_43_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_3565_perm_0 = const()[name = string("op_3565_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3566 = const()[name = string("op_3566"), val = tensor([1, -1, 1024])]; + tensor var_3565_cast_fp16 = transpose(perm = var_3565_perm_0, x = x_453_cast_fp16)[name = string("transpose_168")]; + tensor input_1075_cast_fp16 = reshape(shape = var_3566, x = var_3565_cast_fp16)[name = string("input_1075_cast_fp16")]; + tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341764800))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341895936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342424192))))[name = string("module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1075_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1079_cast_fp16")]; + tensor x_457_axes_0 = const()[name = string("x_457_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("module_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342440640)))]; + tensor module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("module_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342442752)))]; + tensor x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = string("x_457_cast_fp16")]; + tensor input_1081_perm_0 = const()[name = string("input_1081_perm_0"), val = tensor([0, 2, 1])]; + string input_1083_pad_type_0 = const()[name = string("input_1083_pad_type_0"), val = string("valid")]; + tensor input_1083_strides_0 = const()[name = string("input_1083_strides_0"), val = tensor([1])]; + tensor input_1083_pad_0 = const()[name = string("input_1083_pad_0"), val = tensor([0, 0])]; + tensor input_1083_dilations_0 = const()[name = string("input_1083_dilations_0"), val = tensor([1])]; + int32 input_1083_groups_0 = const()[name = string("input_1083_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342444864))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342707072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343761216))))[name = string("module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = string("transpose_167")]; + tensor input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1081_cast_fp16)[name = string("input_1083_cast_fp16")]; + int32 x_459_split_num_splits_0 = const()[name = string("x_459_split_num_splits_0"), val = int32(2)]; + int32 x_459_split_axis_0 = const()[name = string("x_459_split_axis_0"), val = int32(1)]; + tensor x_459_split_cast_fp16_0, tensor x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = string("x_459_split_cast_fp16")]; + tensor x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = string("x_459_split_1_sigmoid_cast_fp16")]; + tensor x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_328)[name = string("input_1085_cast_fp16")]; + tensor input_1087_pad_0 = const()[name = string("input_1087_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1087_mode_0 = const()[name = string("input_1087_mode_0"), val = string("constant")]; + fp16 const_217_to_fp16 = const()[name = string("const_217_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = string("input_1087_cast_fp16")]; + string input_1089_pad_type_0 = const()[name = string("input_1089_pad_type_0"), val = string("valid")]; + int32 input_1089_groups_0 = const()[name = string("input_1089_groups_0"), val = int32(1024)]; + tensor input_1089_strides_0 = const()[name = string("input_1089_strides_0"), val = tensor([1])]; + tensor input_1089_pad_0 = const()[name = string("input_1089_pad_0"), val = tensor([0, 0])]; + tensor input_1089_dilations_0 = const()[name = string("input_1089_dilations_0"), val = tensor([1])]; + tensor const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343794048)))]; + tensor const_289_to_fp16 = const()[name = string("const_289_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343812544)))]; + tensor input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16, x = input_1087_cast_fp16)[name = string("input_1091_cast_fp16")]; + tensor input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = string("input_1093_cast_fp16")]; + string x_461_pad_type_0 = const()[name = string("x_461_pad_type_0"), val = string("valid")]; + tensor x_461_strides_0 = const()[name = string("x_461_strides_0"), val = tensor([1])]; + tensor x_461_pad_0 = const()[name = string("x_461_pad_0"), val = tensor([0, 0])]; + tensor x_461_dilations_0 = const()[name = string("x_461_dilations_0"), val = tensor([1])]; + int32 x_461_groups_0 = const()[name = string("x_461_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343814656))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343945792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344474112))))[name = string("module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1093_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = string("transpose_166")]; + tensor input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = string("input_1097_cast_fp16")]; + tensor input_1099_axes_0 = const()[name = string("input_1099_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344490560)))]; + tensor module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344492672)))]; + tensor input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344494784))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345019136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347127680))))[name = string("module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1099_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347193280))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347717632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349826560))))[name = string("module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1103_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_3626_to_fp16 = const()[name = string("op_3626_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3627_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3626_to_fp16)[name = string("op_3627_cast_fp16")]; + tensor input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3627_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor input_1111_axes_0 = const()[name = string("input_1111_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_out_weight_to_fp16 = const()[name = string("module_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349843008)))]; + tensor module_layers_20_norm_out_bias_to_fp16 = const()[name = string("module_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349845120)))]; + tensor input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = string("input_1111_cast_fp16")]; + tensor input_1113_axes_0 = const()[name = string("input_1113_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349847232)))]; + tensor module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349849344)))]; + tensor input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = string("input_1113_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349851456))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350375808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352486528))))[name = string("module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1113_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352552128))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353076480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355185088))))[name = string("module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1117_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_3655_to_fp16 = const()[name = string("op_3655_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3656_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3655_to_fp16)[name = string("op_3656_cast_fp16")]; + tensor input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3656_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355201536)))]; + tensor module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355203648)))]; + tensor query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = string("query_43_cast_fp16")]; + tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355205760))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355336896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355862976))))[name = string("module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_3672 = const()[name = string("op_3672"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_3672, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355879424))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356010560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356538496))))[name = string("module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_3676 = const()[name = string("op_3676"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_3676, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356554944))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356686080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357210624))))[name = string("module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_3680 = const()[name = string("op_3680"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_3680, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357227072)))]; + tensor var_3692_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_3692_cast_fp16")]; + tensor module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357229184)))]; + tensor var_3694_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_3694_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_469_transpose_x_0 = const()[name = string("x_469_transpose_x_0"), val = bool(false)]; + bool x_469_transpose_y_0 = const()[name = string("x_469_transpose_y_0"), val = bool(false)]; + tensor var_3696_to_fp16 = const()[name = string("op_3696_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357231296)))]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3694_cast_fp16)[name = string("transpose_165")]; + tensor x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = var_3696_to_fp16)[name = string("x_469_cast_fp16")]; + tensor x_471_pad_0 = const()[name = string("x_471_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_471_mode_0 = const()[name = string("x_471_mode_0"), val = string("constant")]; + fp16 const_224_to_fp16 = const()[name = string("const_224_to_fp16"), val = fp16(0x0p+0)]; + tensor x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")]; + tensor var_3704 = const()[name = string("op_3704"), val = tensor([1, 8, -1, 188])]; + tensor x_473_cast_fp16 = reshape(shape = var_3704, x = x_471_cast_fp16)[name = string("x_473_cast_fp16")]; + tensor var_3708_begin_0 = const()[name = string("op_3708_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3708_end_0 = const()[name = string("op_3708_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3708_end_mask_0 = const()[name = string("op_3708_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3708_cast_fp16 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = x_473_cast_fp16)[name = string("op_3708_cast_fp16")]; + tensor var_3709 = const()[name = string("op_3709"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_3709, x = var_3708_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3692_cast_fp16)[name = string("transpose_164")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_3718_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_3718_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_3718_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = string("scores_87_cast_fp16")]; + tensor var_3724_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = string("op_3724_cast_fp16")]; + tensor input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3724_cast_fp16, cond = mask_3)[name = string("input_1125_cast_fp16")]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_45_cast_fp16)[name = string("x_475_cast_fp16")]; + tensor var_3728_perm_0 = const()[name = string("op_3728_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3729 = const()[name = string("op_3729"), val = tensor([1, -1, 1024])]; + tensor var_3728_cast_fp16 = transpose(perm = var_3728_perm_0, x = x_475_cast_fp16)[name = string("transpose_161")]; + tensor input_1127_cast_fp16 = reshape(shape = var_3729, x = var_3728_cast_fp16)[name = string("input_1127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357999360))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358130496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358657984))))[name = string("module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1127_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1131_cast_fp16")]; + tensor x_479_axes_0 = const()[name = string("x_479_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("module_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358674432)))]; + tensor module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("module_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358676544)))]; + tensor x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor input_1133_perm_0 = const()[name = string("input_1133_perm_0"), val = tensor([0, 2, 1])]; + string input_1135_pad_type_0 = const()[name = string("input_1135_pad_type_0"), val = string("valid")]; + tensor input_1135_strides_0 = const()[name = string("input_1135_strides_0"), val = tensor([1])]; + tensor input_1135_pad_0 = const()[name = string("input_1135_pad_0"), val = tensor([0, 0])]; + tensor input_1135_dilations_0 = const()[name = string("input_1135_dilations_0"), val = tensor([1])]; + int32 input_1135_groups_0 = const()[name = string("input_1135_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358678656))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358940864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359991936))))[name = string("module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = string("transpose_160")]; + tensor input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1133_cast_fp16)[name = string("input_1135_cast_fp16")]; + int32 x_481_split_num_splits_0 = const()[name = string("x_481_split_num_splits_0"), val = int32(2)]; + int32 x_481_split_axis_0 = const()[name = string("x_481_split_axis_0"), val = int32(1)]; + tensor x_481_split_cast_fp16_0, tensor x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = string("x_481_split_cast_fp16")]; + tensor x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = string("x_481_split_1_sigmoid_cast_fp16")]; + tensor x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_328)[name = string("input_1137_cast_fp16")]; + tensor input_1139_pad_0 = const()[name = string("input_1139_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1139_mode_0 = const()[name = string("input_1139_mode_0"), val = string("constant")]; + fp16 const_227_to_fp16 = const()[name = string("const_227_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = string("input_1139_cast_fp16")]; + string input_1141_pad_type_0 = const()[name = string("input_1141_pad_type_0"), val = string("valid")]; + int32 input_1141_groups_0 = const()[name = string("input_1141_groups_0"), val = int32(1024)]; + tensor input_1141_strides_0 = const()[name = string("input_1141_strides_0"), val = tensor([1])]; + tensor input_1141_pad_0 = const()[name = string("input_1141_pad_0"), val = tensor([0, 0])]; + tensor input_1141_dilations_0 = const()[name = string("input_1141_dilations_0"), val = tensor([1])]; + tensor const_290_to_fp16 = const()[name = string("const_290_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360024768)))]; + tensor const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360043264)))]; + tensor input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16, x = input_1139_cast_fp16)[name = string("input_1143_cast_fp16")]; + tensor input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = string("input_1145_cast_fp16")]; + string x_483_pad_type_0 = const()[name = string("x_483_pad_type_0"), val = string("valid")]; + tensor x_483_strides_0 = const()[name = string("x_483_strides_0"), val = tensor([1])]; + tensor x_483_pad_0 = const()[name = string("x_483_pad_0"), val = tensor([0, 0])]; + tensor x_483_dilations_0 = const()[name = string("x_483_dilations_0"), val = tensor([1])]; + int32 x_483_groups_0 = const()[name = string("x_483_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360045376))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360176512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360705280))))[name = string("module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1145_cast_fp16)[name = string("x_483_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = string("transpose_159")]; + tensor input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = string("input_1149_cast_fp16")]; + tensor input_1151_axes_0 = const()[name = string("input_1151_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360721728)))]; + tensor module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360723840)))]; + tensor input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360725952))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361250304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363351872))))[name = string("module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1151_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363417472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363941824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366050368))))[name = string("module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1155_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_3789_to_fp16 = const()[name = string("op_3789_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3790_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3789_to_fp16)[name = string("op_3790_cast_fp16")]; + tensor input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3790_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor input_1163_axes_0 = const()[name = string("input_1163_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_out_weight_to_fp16 = const()[name = string("module_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366066816)))]; + tensor module_layers_21_norm_out_bias_to_fp16 = const()[name = string("module_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366068928)))]; + tensor input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = string("input_1163_cast_fp16")]; + tensor input_1165_axes_0 = const()[name = string("input_1165_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366071040)))]; + tensor module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366073152)))]; + tensor input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = string("input_1165_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366075264))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366599616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368701440))))[name = string("module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1165_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368767040))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369291392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371400640))))[name = string("module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1169_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_3818_to_fp16 = const()[name = string("op_3818_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3819_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3818_to_fp16)[name = string("op_3819_cast_fp16")]; + tensor input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3819_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371417088)))]; + tensor module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371419200)))]; + tensor query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = string("query_45_cast_fp16")]; + tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371421312))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371552448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372075456))))[name = string("module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_3835 = const()[name = string("op_3835"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_3835, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372091904))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372223040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372751104))))[name = string("module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_3839 = const()[name = string("op_3839"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_3839, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372767552))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372898688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373419520))))[name = string("module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_3843 = const()[name = string("op_3843"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_3843, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373435968)))]; + tensor var_3855_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_3855_cast_fp16")]; + tensor module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373438080)))]; + tensor var_3857_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_3857_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_491_transpose_x_0 = const()[name = string("x_491_transpose_x_0"), val = bool(false)]; + bool x_491_transpose_y_0 = const()[name = string("x_491_transpose_y_0"), val = bool(false)]; + tensor var_3859_to_fp16 = const()[name = string("op_3859_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373440192)))]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3857_cast_fp16)[name = string("transpose_158")]; + tensor x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = var_3859_to_fp16)[name = string("x_491_cast_fp16")]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_493_mode_0 = const()[name = string("x_493_mode_0"), val = string("constant")]; + fp16 const_234_to_fp16 = const()[name = string("const_234_to_fp16"), val = fp16(0x0p+0)]; + tensor x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor var_3867 = const()[name = string("op_3867"), val = tensor([1, 8, -1, 188])]; + tensor x_495_cast_fp16 = reshape(shape = var_3867, x = x_493_cast_fp16)[name = string("x_495_cast_fp16")]; + tensor var_3871_begin_0 = const()[name = string("op_3871_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3871_end_0 = const()[name = string("op_3871_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3871_end_mask_0 = const()[name = string("op_3871_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3871_cast_fp16 = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = x_495_cast_fp16)[name = string("op_3871_cast_fp16")]; + tensor var_3872 = const()[name = string("op_3872"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_3872, x = var_3871_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3855_cast_fp16)[name = string("transpose_157")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_3881_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_3881_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_3881_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = string("scores_91_cast_fp16")]; + tensor var_3887_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = string("op_3887_cast_fp16")]; + tensor input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_3887_cast_fp16, cond = mask_3)[name = string("input_1177_cast_fp16")]; + bool x_497_transpose_x_0 = const()[name = string("x_497_transpose_x_0"), val = bool(false)]; + bool x_497_transpose_y_0 = const()[name = string("x_497_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")]; + tensor x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_47_cast_fp16)[name = string("x_497_cast_fp16")]; + tensor var_3891_perm_0 = const()[name = string("op_3891_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3892 = const()[name = string("op_3892"), val = tensor([1, -1, 1024])]; + tensor var_3891_cast_fp16 = transpose(perm = var_3891_perm_0, x = x_497_cast_fp16)[name = string("transpose_154")]; + tensor input_1179_cast_fp16 = reshape(shape = var_3892, x = var_3891_cast_fp16)[name = string("input_1179_cast_fp16")]; + tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374208256))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374339392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374867456))))[name = string("module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1179_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1183_cast_fp16")]; + tensor x_501_axes_0 = const()[name = string("x_501_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("module_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374883904)))]; + tensor module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("module_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374886016)))]; + tensor x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = string("x_501_cast_fp16")]; + tensor input_1185_perm_0 = const()[name = string("input_1185_perm_0"), val = tensor([0, 2, 1])]; + string input_1187_pad_type_0 = const()[name = string("input_1187_pad_type_0"), val = string("valid")]; + tensor input_1187_strides_0 = const()[name = string("input_1187_strides_0"), val = tensor([1])]; + tensor input_1187_pad_0 = const()[name = string("input_1187_pad_0"), val = tensor([0, 0])]; + tensor input_1187_dilations_0 = const()[name = string("input_1187_dilations_0"), val = tensor([1])]; + int32 input_1187_groups_0 = const()[name = string("input_1187_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374888128))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375150336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376195840))))[name = string("module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = string("transpose_153")]; + tensor input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1185_cast_fp16)[name = string("input_1187_cast_fp16")]; + int32 x_503_split_num_splits_0 = const()[name = string("x_503_split_num_splits_0"), val = int32(2)]; + int32 x_503_split_axis_0 = const()[name = string("x_503_split_axis_0"), val = int32(1)]; + tensor x_503_split_cast_fp16_0, tensor x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = string("x_503_split_cast_fp16")]; + tensor x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = string("x_503_split_1_sigmoid_cast_fp16")]; + tensor x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_328)[name = string("input_1189_cast_fp16")]; + tensor input_1191_pad_0 = const()[name = string("input_1191_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1191_mode_0 = const()[name = string("input_1191_mode_0"), val = string("constant")]; + fp16 const_237_to_fp16 = const()[name = string("const_237_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = string("input_1191_cast_fp16")]; + string input_1193_pad_type_0 = const()[name = string("input_1193_pad_type_0"), val = string("valid")]; + int32 input_1193_groups_0 = const()[name = string("input_1193_groups_0"), val = int32(1024)]; + tensor input_1193_strides_0 = const()[name = string("input_1193_strides_0"), val = tensor([1])]; + tensor input_1193_pad_0 = const()[name = string("input_1193_pad_0"), val = tensor([0, 0])]; + tensor input_1193_dilations_0 = const()[name = string("input_1193_dilations_0"), val = tensor([1])]; + tensor const_292_to_fp16 = const()[name = string("const_292_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376228672)))]; + tensor const_293_to_fp16 = const()[name = string("const_293_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376247168)))]; + tensor input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16, x = input_1191_cast_fp16)[name = string("input_1195_cast_fp16")]; + tensor input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = string("input_1197_cast_fp16")]; + string x_505_pad_type_0 = const()[name = string("x_505_pad_type_0"), val = string("valid")]; + tensor x_505_strides_0 = const()[name = string("x_505_strides_0"), val = tensor([1])]; + tensor x_505_pad_0 = const()[name = string("x_505_pad_0"), val = tensor([0, 0])]; + tensor x_505_dilations_0 = const()[name = string("x_505_dilations_0"), val = tensor([1])]; + int32 x_505_groups_0 = const()[name = string("x_505_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376249280))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376380416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376909312))))[name = string("module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1197_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = string("transpose_152")]; + tensor input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = string("input_1201_cast_fp16")]; + tensor input_1203_axes_0 = const()[name = string("input_1203_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376925760)))]; + tensor module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376927872)))]; + tensor input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376929984))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377454336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379556160))))[name = string("module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1203_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379621760))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380146112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382257152))))[name = string("module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1207_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_3952_to_fp16 = const()[name = string("op_3952_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3953_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3952_to_fp16)[name = string("op_3953_cast_fp16")]; + tensor input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3953_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor input_1215_axes_0 = const()[name = string("input_1215_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_out_weight_to_fp16 = const()[name = string("module_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382273600)))]; + tensor module_layers_22_norm_out_bias_to_fp16 = const()[name = string("module_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382275712)))]; + tensor input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = string("input_1215_cast_fp16")]; + tensor input_1217_axes_0 = const()[name = string("input_1217_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382277824)))]; + tensor module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382279936)))]; + tensor input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = string("input_1217_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382282048))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382806400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384914560))))[name = string("module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1217_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384980160))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385504512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387615552))))[name = string("module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1221_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_3981_to_fp16 = const()[name = string("op_3981_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3982_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3981_to_fp16)[name = string("op_3982_cast_fp16")]; + tensor input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3982_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor query_axes_0 = const()[name = string("query_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387632000)))]; + tensor module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387634112)))]; + tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = string("query_cast_fp16")]; + tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387636224))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387767360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388287232))))[name = string("module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_3998 = const()[name = string("op_3998"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_3998, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388303680))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388434816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388962304))))[name = string("module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_4002 = const()[name = string("op_4002"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_4002, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388978752))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389109888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389628736))))[name = string("module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_4006 = const()[name = string("op_4006"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_4006, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389645184)))]; + tensor var_4018_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4018_cast_fp16")]; + tensor module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389647296)))]; + tensor var_4020_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4020_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_513_transpose_x_0 = const()[name = string("x_513_transpose_x_0"), val = bool(false)]; + bool x_513_transpose_y_0 = const()[name = string("x_513_transpose_y_0"), val = bool(false)]; + tensor var_4022_to_fp16 = const()[name = string("op_4022_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389649408)))]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4020_cast_fp16)[name = string("transpose_151")]; + tensor x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_4022_to_fp16)[name = string("x_513_cast_fp16")]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_515_mode_0 = const()[name = string("x_515_mode_0"), val = string("constant")]; + fp16 const_244_to_fp16 = const()[name = string("const_244_to_fp16"), val = fp16(0x0p+0)]; + tensor x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor var_4030 = const()[name = string("op_4030"), val = tensor([1, 8, -1, 188])]; + tensor x_517_cast_fp16 = reshape(shape = var_4030, x = x_515_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor var_4034_begin_0 = const()[name = string("op_4034_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4034_end_0 = const()[name = string("op_4034_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_4034_end_mask_0 = const()[name = string("op_4034_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4034_cast_fp16 = slice_by_index(begin = var_4034_begin_0, end = var_4034_end_0, end_mask = var_4034_end_mask_0, x = x_517_cast_fp16)[name = string("op_4034_cast_fp16")]; + tensor var_4035 = const()[name = string("op_4035"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4035, x = var_4034_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4018_cast_fp16)[name = string("transpose_150")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_4044_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4044_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_4044_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = string("scores_cast_fp16")]; + tensor var_4050_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_4050_cast_fp16")]; + tensor input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4050_cast_fp16, cond = mask_3)[name = string("input_1229_cast_fp16")]; + bool x_519_transpose_x_0 = const()[name = string("x_519_transpose_x_0"), val = bool(false)]; + bool x_519_transpose_y_0 = const()[name = string("x_519_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")]; + tensor x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor var_4054_perm_0 = const()[name = string("op_4054_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4055 = const()[name = string("op_4055"), val = tensor([1, -1, 1024])]; + tensor var_4054_cast_fp16 = transpose(perm = var_4054_perm_0, x = x_519_cast_fp16)[name = string("transpose_147")]; + tensor input_1231_cast_fp16 = reshape(shape = var_4055, x = var_4054_cast_fp16)[name = string("input_1231_cast_fp16")]; + tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390417472))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390548608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391069632))))[name = string("module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1231_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1235_cast_fp16")]; + tensor x_523_axes_0 = const()[name = string("x_523_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("module_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391086080)))]; + tensor module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("module_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391088192)))]; + tensor x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = string("x_523_cast_fp16")]; + tensor input_1237_perm_0 = const()[name = string("input_1237_perm_0"), val = tensor([0, 2, 1])]; + string input_1239_pad_type_0 = const()[name = string("input_1239_pad_type_0"), val = string("valid")]; + tensor input_1239_strides_0 = const()[name = string("input_1239_strides_0"), val = tensor([1])]; + tensor input_1239_pad_0 = const()[name = string("input_1239_pad_0"), val = tensor([0, 0])]; + tensor input_1239_dilations_0 = const()[name = string("input_1239_dilations_0"), val = tensor([1])]; + int32 input_1239_groups_0 = const()[name = string("input_1239_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391090304))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391352512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392398272))))[name = string("module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = string("transpose_146")]; + tensor input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1237_cast_fp16)[name = string("input_1239_cast_fp16")]; + int32 x_525_split_num_splits_0 = const()[name = string("x_525_split_num_splits_0"), val = int32(2)]; + int32 x_525_split_axis_0 = const()[name = string("x_525_split_axis_0"), val = int32(1)]; + tensor x_525_split_cast_fp16_0, tensor x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = string("x_525_split_cast_fp16")]; + tensor x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = string("x_525_split_1_sigmoid_cast_fp16")]; + tensor x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = string("x_525_cast_fp16")]; + tensor input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_328)[name = string("input_1241_cast_fp16")]; + tensor input_1243_pad_0 = const()[name = string("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1243_mode_0 = const()[name = string("input_1243_mode_0"), val = string("constant")]; + fp16 const_247_to_fp16 = const()[name = string("const_247_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = string("input_1243_cast_fp16")]; + string input_1245_pad_type_0 = const()[name = string("input_1245_pad_type_0"), val = string("valid")]; + int32 input_1245_groups_0 = const()[name = string("input_1245_groups_0"), val = int32(1024)]; + tensor input_1245_strides_0 = const()[name = string("input_1245_strides_0"), val = tensor([1])]; + tensor input_1245_pad_0 = const()[name = string("input_1245_pad_0"), val = tensor([0, 0])]; + tensor input_1245_dilations_0 = const()[name = string("input_1245_dilations_0"), val = tensor([1])]; + tensor const_294_to_fp16 = const()[name = string("const_294_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392431104)))]; + tensor const_295_to_fp16 = const()[name = string("const_295_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392449600)))]; + tensor input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16, x = input_1243_cast_fp16)[name = string("input_1247_cast_fp16")]; + tensor input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = string("input_1249_cast_fp16")]; + string x_527_pad_type_0 = const()[name = string("x_527_pad_type_0"), val = string("valid")]; + tensor x_527_strides_0 = const()[name = string("x_527_strides_0"), val = tensor([1])]; + tensor x_527_pad_0 = const()[name = string("x_527_pad_0"), val = tensor([0, 0])]; + tensor x_527_dilations_0 = const()[name = string("x_527_dilations_0"), val = tensor([1])]; + int32 x_527_groups_0 = const()[name = string("x_527_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392451712))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392582848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393112704))))[name = string("module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1249_cast_fp16)[name = string("x_527_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = string("transpose_145")]; + tensor input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = string("input_1253_cast_fp16")]; + tensor input_1255_axes_0 = const()[name = string("input_1255_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393129152)))]; + tensor module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393131264)))]; + tensor input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393133376))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393657728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395758400))))[name = string("module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1255_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1 = constexpr_lut_to_sparse(indices_mask = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395824000))), indices_nonzero_data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396348352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398447296))))[name = string("module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut")]; + tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_sparse_to_dense(mask = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_0, nonzero_data = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue_lut_1)[name = string("module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1259_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_4115_to_fp16 = const()[name = string("op_4115_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4116_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4115_to_fp16)[name = string("op_4116_cast_fp16")]; + tensor input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4116_cast_fp16)[name = string("input_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_out_weight_to_fp16 = const()[name = string("module_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398463744)))]; + tensor module_layers_23_norm_out_bias_to_fp16 = const()[name = string("module_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398465856)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")]; + tensor obj_1_perm_0 = const()[name = string("obj_1_perm_0"), val = tensor([0, 2, 1])]; + string obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")]; + tensor encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = string("cast_0")]; + } -> (encoder, encoder_length); +} \ No newline at end of file diff --git a/bark-3.0/Encoder.mlmodelc/weights/weight.bin b/bark-3.0/Encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..9c4f32ee290c038868763536f63e9df0f58ce688 --- /dev/null +++ b/bark-3.0/Encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0c050671b447917966d4c01ef4a5ccef671115735d9b298b6ec965caa681c27 +size 398467968 diff --git a/bark-3.0/Encoder_lut3.mlmodelc/analytics/coremldata.bin b/bark-3.0/Encoder_lut3.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cdfd3460305f308090cab53deca41d7fb1522ed2 --- /dev/null +++ b/bark-3.0/Encoder_lut3.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b20435e51a1ec8f1791a99f105633e322b7ef7e52d295977c8aecb1c49d6c14d +size 243 diff --git a/bark-3.0/Encoder_lut3.mlmodelc/coremldata.bin b/bark-3.0/Encoder_lut3.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..dc47044230ddc633e9eeb280058d0258e3aa3e78 --- /dev/null +++ b/bark-3.0/Encoder_lut3.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:101aea9493158e55e4de898e472e33cf04aa4a5307f755fcb2b96241822fa4eb +size 543 diff --git a/bark-3.0/Encoder_lut3.mlmodelc/model.mil b/bark-3.0/Encoder_lut3.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..94ef603628c39bb8f0216ca40d052bcec9533b3d --- /dev/null +++ b/bark-3.0/Encoder_lut3.mlmodelc/model.mil @@ -0,0 +1,3404 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor mel, tensor mel_length) { + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + string var_86_to_fp16_dtype_0 = const()[name = string("op_86_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor mel_length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = mel_length)[name = string("cast_3")]; + tensor var_88_cast_fp16 = add(x = mel_length_to_fp16, y = var_87_promoted_to_fp16)[name = string("op_88_cast_fp16")]; + fp16 _inversed_90_y_0_to_fp16 = const()[name = string("_inversed_90_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = string("_inversed_90_cast_fp16")]; + fp16 var_91_to_fp16 = const()[name = string("op_91_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = string("lengths_1_cast_fp16")]; + tensor lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = string("lengths_3_cast_fp16")]; + fp16 var_95_promoted_to_fp16 = const()[name = string("op_95_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = string("op_96_cast_fp16")]; + fp16 _inversed_98_y_0_to_fp16 = const()[name = string("_inversed_98_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = string("_inversed_98_cast_fp16")]; + fp16 var_99_to_fp16 = const()[name = string("op_99_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = string("lengths_7_cast_fp16")]; + tensor lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = string("lengths_9_cast_fp16")]; + fp16 var_103_promoted_to_fp16 = const()[name = string("op_103_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = string("op_104_cast_fp16")]; + fp16 _inversed_106_y_0_to_fp16 = const()[name = string("_inversed_106_y_0_to_fp16"), val = fp16(0x1p-1)]; + tensor _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = string("_inversed_106_cast_fp16")]; + fp16 var_107_to_fp16 = const()[name = string("op_107_to_fp16"), val = fp16(0x1p+0)]; + tensor lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = string("lengths_13_cast_fp16")]; + tensor lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = string("lengths_cast_fp16")]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_2")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_315")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = string("input_1_cast_fp16")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([2, 2])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = string("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4736)))]; + tensor input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor([2, 2])]; + int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(256)]; + tensor input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = string("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5312)))]; + tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9984)))]; + tensor input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = string("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10560)))]; + tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141696)))]; + tensor input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")]; + tensor input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor([2, 2])]; + int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(256)]; + tensor input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor([1, 1])]; + tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = string("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142272)))]; + tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146944)))]; + tensor input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = string("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147520)))]; + tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278656)))]; + tensor input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_157_perm_0 = const()[name = string("op_157_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_158 = const()[name = string("op_158"), val = tensor([1, 188, -1])]; + tensor var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = string("transpose_314")]; + tensor input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor module_pre_encode_out_weight_to_fp16 = const()[name = string("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279232)))]; + tensor module_pre_encode_out_bias_to_fp16 = const()[name = string("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8667904)))]; + tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = string("linear_0_cast_fp16")]; + string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670016)))]; + tensor var_196_axes_0 = const()[name = string("op_196_axes_0"), val = tensor([-1])]; + tensor encoder_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = string("cast_1")]; + tensor var_196 = expand_dims(axes = var_196_axes_0, x = encoder_length)[name = string("op_196")]; + tensor pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = string("pad_mask_1")]; + tensor var_198_axes_0 = const()[name = string("op_198_axes_0"), val = tensor([1])]; + tensor var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = string("op_198")]; + tensor var_199 = const()[name = string("op_199"), val = tensor([1, 188, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = string("pad_mask_for_att_mask_1")]; + tensor var_201_perm_0 = const()[name = string("op_201_perm_0"), val = tensor([0, 2, 1])]; + tensor var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = string("pad_mask_for_att_mask")]; + tensor const_7 = const()[name = string("const_7"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = string("att_mask")]; + tensor mask_1 = logical_not(x = att_mask)[name = string("mask_1")]; + tensor pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")]; + tensor input_21_axes_0 = const()[name = string("input_21_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8670848)))]; + tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8672960)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8675072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10248000))))[name = string("module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_1_bias_0_to_fp16 = const()[name = string("linear_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10313600)))]; + tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_21_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10321856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11894784))))[name = string("module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11911232)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_25_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_232_to_fp16 = const()[name = string("op_232_to_fp16"), val = fp16(0x1p-1)]; + tensor var_233_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_232_to_fp16)[name = string("op_233_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_233_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11913344)))]; + tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11915456)))]; + tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = string("query_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11917568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12310848))))[name = string("module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_249 = const()[name = string("op_249"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_249, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12327296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12720576))))[name = string("module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_253, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12737024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13130304))))[name = string("module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_257 = const()[name = string("op_257"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_257, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13146752)))]; + tensor var_269_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_269_cast_fp16")]; + tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13148864)))]; + tensor var_271_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_271_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor var_273_to_fp16 = const()[name = string("op_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13150976)))]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_271_cast_fp16)[name = string("transpose_312")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_273_to_fp16)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_281 = const()[name = string("op_281"), val = tensor([1, 8, -1, 188])]; + tensor x_11_cast_fp16 = reshape(shape = var_281, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_285_begin_0 = const()[name = string("op_285_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_285_end_0 = const()[name = string("op_285_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_285_end_mask_0 = const()[name = string("op_285_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_285_cast_fp16 = slice_by_index(begin = var_285_begin_0, end = var_285_end_0, end_mask = var_285_end_mask_0, x = x_11_cast_fp16)[name = string("op_285_cast_fp16")]; + tensor var_286 = const()[name = string("op_286"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_286, x = var_285_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_269_cast_fp16)[name = string("transpose_311")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_295_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_295_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_295_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_3_axes_0 = const()[name = string("mask_3_axes_0"), val = tensor([1])]; + tensor mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = string("mask_3")]; + fp16 var_12_to_fp16 = const()[name = string("op_12_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = string("scores_3_cast_fp16")]; + tensor var_301_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_301_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_301_cast_fp16, cond = mask_3)[name = string("input_33_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_305_perm_0 = const()[name = string("op_305_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_306 = const()[name = string("op_306"), val = tensor([1, -1, 1024])]; + tensor var_305_cast_fp16 = transpose(perm = var_305_perm_0, x = x_13_cast_fp16)[name = string("transpose_308")]; + tensor input_35_cast_fp16 = reshape(shape = var_306, x = var_305_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13919040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14312320))))[name = string("module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14328768)))]; + tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14330880)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_41_perm_0 = const()[name = string("input_41_perm_0"), val = tensor([0, 2, 1])]; + string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")]; + tensor input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor([1])]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0])]; + tensor input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor([1])]; + int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14332992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15119488))))[name = string("module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = string("transpose_307")]; + tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; + tensor var_328 = expand_dims(axes = var_328_axes_0, x = pad_mask)[name = string("op_328")]; + tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_328)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("constant")]; + fp16 const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1024)]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1])]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([0, 0])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([1])]; + tensor const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15152320)))]; + tensor const_249_to_fp16 = const()[name = string("const_249_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15170816)))]; + tensor input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15172928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15566208))))[name = string("module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_53_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_306")]; + tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor input_59_axes_0 = const()[name = string("input_59_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15582656)))]; + tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15584768)))]; + tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15586880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17159808))))[name = string("module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_59_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17225408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18798336))))[name = string("module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_63_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_366_to_fp16 = const()[name = string("op_366_to_fp16"), val = fp16(0x1p-1)]; + tensor var_367_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_366_to_fp16)[name = string("op_367_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_367_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = string("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18814784)))]; + tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = string("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18816896)))]; + tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor input_73_axes_0 = const()[name = string("input_73_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18819008)))]; + tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18821120)))]; + tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18823232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20396160))))[name = string("module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_73_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20461760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22034688))))[name = string("module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_77_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p-1)]; + tensor var_396_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_396_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22051136)))]; + tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22053248)))]; + tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = string("query_3_cast_fp16")]; + tensor module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22055360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22448640))))[name = string("module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_412, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22465088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22858368))))[name = string("module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_416 = const()[name = string("op_416"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_416, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22874816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23268096))))[name = string("module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_420 = const()[name = string("op_420"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_420, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23284544)))]; + tensor var_432_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_432_cast_fp16")]; + tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23286656)))]; + tensor var_434_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_434_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_29_transpose_x_0 = const()[name = string("x_29_transpose_x_0"), val = bool(false)]; + bool x_29_transpose_y_0 = const()[name = string("x_29_transpose_y_0"), val = bool(false)]; + tensor var_436_to_fp16 = const()[name = string("op_436_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23288768)))]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_434_cast_fp16)[name = string("transpose_305")]; + tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_436_to_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_31_mode_0 = const()[name = string("x_31_mode_0"), val = string("constant")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_444 = const()[name = string("op_444"), val = tensor([1, 8, -1, 188])]; + tensor x_33_cast_fp16 = reshape(shape = var_444, x = x_31_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_448_begin_0 = const()[name = string("op_448_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_448_end_0 = const()[name = string("op_448_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_448_end_mask_0 = const()[name = string("op_448_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_448_cast_fp16 = slice_by_index(begin = var_448_begin_0, end = var_448_end_0, end_mask = var_448_end_mask_0, x = x_33_cast_fp16)[name = string("op_448_cast_fp16")]; + tensor var_449 = const()[name = string("op_449"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_449, x = var_448_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_432_cast_fp16)[name = string("transpose_304")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_458_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_458_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_458_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = string("scores_7_cast_fp16")]; + tensor var_464_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_464_cast_fp16, cond = mask_3)[name = string("input_85_cast_fp16")]; + bool x_35_transpose_x_0 = const()[name = string("x_35_transpose_x_0"), val = bool(false)]; + bool x_35_transpose_y_0 = const()[name = string("x_35_transpose_y_0"), val = bool(false)]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")]; + tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_468_perm_0 = const()[name = string("op_468_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_469 = const()[name = string("op_469"), val = tensor([1, -1, 1024])]; + tensor var_468_cast_fp16 = transpose(perm = var_468_perm_0, x = x_35_cast_fp16)[name = string("transpose_301")]; + tensor input_87_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24056832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24450112))))[name = string("module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_87_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24466560)))]; + tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24468672)))]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([1])]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([0, 0])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24470784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25257280))))[name = string("module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = string("transpose_300")]; + tensor input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")]; + int32 x_41_split_num_splits_0 = const()[name = string("x_41_split_num_splits_0"), val = int32(2)]; + int32 x_41_split_axis_0 = const()[name = string("x_41_split_axis_0"), val = int32(1)]; + tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = string("x_41_split_cast_fp16")]; + tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = string("x_41_split_1_sigmoid_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_328)[name = string("input_97_cast_fp16")]; + tensor input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("constant")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1024)]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1])]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([0, 0])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1])]; + tensor const_250_to_fp16 = const()[name = string("const_250_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25290112)))]; + tensor const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25308608)))]; + tensor input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16, x = input_99_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_43_pad_type_0 = const()[name = string("x_43_pad_type_0"), val = string("valid")]; + tensor x_43_strides_0 = const()[name = string("x_43_strides_0"), val = tensor([1])]; + tensor x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor([0, 0])]; + tensor x_43_dilations_0 = const()[name = string("x_43_dilations_0"), val = tensor([1])]; + int32 x_43_groups_0 = const()[name = string("x_43_groups_0"), val = int32(1)]; + tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25310720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25704000))))[name = string("module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_105_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = string("transpose_299")]; + tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor input_111_axes_0 = const()[name = string("input_111_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25720448)))]; + tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25722560)))]; + tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25724672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27297600))))[name = string("module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_111_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27363200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28936128))))[name = string("module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_115_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1p-1)]; + tensor var_530_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_530_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor input_123_axes_0 = const()[name = string("input_123_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = string("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28952576)))]; + tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = string("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28954688)))]; + tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28956800)))]; + tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28958912)))]; + tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28961024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30533952))))[name = string("module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_125_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30599552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32172480))))[name = string("module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_129_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_558_to_fp16 = const()[name = string("op_558_to_fp16"), val = fp16(0x1p-1)]; + tensor var_559_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_558_to_fp16)[name = string("op_559_cast_fp16")]; + tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_559_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32188928)))]; + tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32191040)))]; + tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = string("query_5_cast_fp16")]; + tensor module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32193152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32586432))))[name = string("module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_575 = const()[name = string("op_575"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_575, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32602880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32996160))))[name = string("module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_579 = const()[name = string("op_579"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_579, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33012608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33405888))))[name = string("module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_583 = const()[name = string("op_583"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_583, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33422336)))]; + tensor var_595_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_595_cast_fp16")]; + tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33424448)))]; + tensor var_597_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_597_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_51_transpose_x_0 = const()[name = string("x_51_transpose_x_0"), val = bool(false)]; + bool x_51_transpose_y_0 = const()[name = string("x_51_transpose_y_0"), val = bool(false)]; + tensor var_599_to_fp16 = const()[name = string("op_599_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33426560)))]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_597_cast_fp16)[name = string("transpose_298")]; + tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_599_to_fp16)[name = string("x_51_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_607 = const()[name = string("op_607"), val = tensor([1, 8, -1, 188])]; + tensor x_55_cast_fp16 = reshape(shape = var_607, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_611_begin_0 = const()[name = string("op_611_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_611_end_0 = const()[name = string("op_611_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_611_end_mask_0 = const()[name = string("op_611_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_611_cast_fp16 = slice_by_index(begin = var_611_begin_0, end = var_611_end_0, end_mask = var_611_end_mask_0, x = x_55_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor var_612 = const()[name = string("op_612"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_612, x = var_611_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_595_cast_fp16)[name = string("transpose_297")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_621_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_621_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_621_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = string("scores_11_cast_fp16")]; + tensor var_627_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_627_cast_fp16, cond = mask_3)[name = string("input_137_cast_fp16")]; + bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)]; + bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)]; + tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")]; + tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_631_perm_0 = const()[name = string("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_632 = const()[name = string("op_632"), val = tensor([1, -1, 1024])]; + tensor var_631_cast_fp16 = transpose(perm = var_631_perm_0, x = x_57_cast_fp16)[name = string("transpose_294")]; + tensor input_139_cast_fp16 = reshape(shape = var_632, x = var_631_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34194624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34587904))))[name = string("module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_139_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34604352)))]; + tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34606464)))]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34608576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35395072))))[name = string("module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = string("transpose_293")]; + tensor input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_145_cast_fp16)[name = string("input_147_cast_fp16")]; + int32 x_63_split_num_splits_0 = const()[name = string("x_63_split_num_splits_0"), val = int32(2)]; + int32 x_63_split_axis_0 = const()[name = string("x_63_split_axis_0"), val = int32(1)]; + tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = string("x_63_split_cast_fp16")]; + tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = string("x_63_split_1_sigmoid_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_328)[name = string("input_149_cast_fp16")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_151_mode_0 = const()[name = string("input_151_mode_0"), val = string("constant")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1024)]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + tensor const_252_to_fp16 = const()[name = string("const_252_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35427904)))]; + tensor const_253_to_fp16 = const()[name = string("const_253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35446400)))]; + tensor input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16, x = input_151_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("valid")]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([0, 0])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35448512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35841792))))[name = string("module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_157_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = string("transpose_292")]; + tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = string("input_161_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35858240)))]; + tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35860352)))]; + tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35862464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37435392))))[name = string("module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_163_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37500992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39073920))))[name = string("module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_167_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_692_to_fp16 = const()[name = string("op_692_to_fp16"), val = fp16(0x1p-1)]; + tensor var_693_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_692_to_fp16)[name = string("op_693_cast_fp16")]; + tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_693_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor input_175_axes_0 = const()[name = string("input_175_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = string("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39090368)))]; + tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = string("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39092480)))]; + tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor input_177_axes_0 = const()[name = string("input_177_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39094592)))]; + tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39096704)))]; + tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39098816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40671744))))[name = string("module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_177_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40737344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42310272))))[name = string("module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_181_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_721_to_fp16 = const()[name = string("op_721_to_fp16"), val = fp16(0x1p-1)]; + tensor var_722_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_721_to_fp16)[name = string("op_722_cast_fp16")]; + tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_722_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42326720)))]; + tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42328832)))]; + tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = string("query_7_cast_fp16")]; + tensor module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42330944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42724224))))[name = string("module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_738, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42740672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43133952))))[name = string("module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_742, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43150400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43543680))))[name = string("module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43560128)))]; + tensor var_758_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_758_cast_fp16")]; + tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562240)))]; + tensor var_760_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_760_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_73_transpose_x_0 = const()[name = string("x_73_transpose_x_0"), val = bool(false)]; + bool x_73_transpose_y_0 = const()[name = string("x_73_transpose_y_0"), val = bool(false)]; + tensor var_762_to_fp16 = const()[name = string("op_762_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43564352)))]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_760_cast_fp16)[name = string("transpose_291")]; + tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_762_to_fp16)[name = string("x_73_cast_fp16")]; + tensor x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_770 = const()[name = string("op_770"), val = tensor([1, 8, -1, 188])]; + tensor x_77_cast_fp16 = reshape(shape = var_770, x = x_75_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_774_begin_0 = const()[name = string("op_774_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_774_end_0 = const()[name = string("op_774_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_774_end_mask_0 = const()[name = string("op_774_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = x_77_cast_fp16)[name = string("op_774_cast_fp16")]; + tensor var_775 = const()[name = string("op_775"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_758_cast_fp16)[name = string("transpose_290")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_784_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_784_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_784_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = string("scores_15_cast_fp16")]; + tensor var_790_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = string("op_790_cast_fp16")]; + tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_790_cast_fp16, cond = mask_3)[name = string("input_189_cast_fp16")]; + bool x_79_transpose_x_0 = const()[name = string("x_79_transpose_x_0"), val = bool(false)]; + bool x_79_transpose_y_0 = const()[name = string("x_79_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")]; + tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor var_794_perm_0 = const()[name = string("op_794_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_795 = const()[name = string("op_795"), val = tensor([1, -1, 1024])]; + tensor var_794_cast_fp16 = transpose(perm = var_794_perm_0, x = x_79_cast_fp16)[name = string("transpose_287")]; + tensor input_191_cast_fp16 = reshape(shape = var_795, x = var_794_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44332416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44725696))))[name = string("module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_191_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor x_83_axes_0 = const()[name = string("x_83_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44742144)))]; + tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44744256)))]; + tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_197_perm_0 = const()[name = string("input_197_perm_0"), val = tensor([0, 2, 1])]; + string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("valid")]; + tensor input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor([1])]; + tensor input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor([0, 0])]; + tensor input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor([1])]; + int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44746368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45532864))))[name = string("module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = string("transpose_286")]; + tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_197_cast_fp16)[name = string("input_199_cast_fp16")]; + int32 x_85_split_num_splits_0 = const()[name = string("x_85_split_num_splits_0"), val = int32(2)]; + int32 x_85_split_axis_0 = const()[name = string("x_85_split_axis_0"), val = int32(1)]; + tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = string("x_85_split_cast_fp16")]; + tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = string("x_85_split_1_sigmoid_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_328)[name = string("input_201_cast_fp16")]; + tensor input_203_pad_0 = const()[name = string("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("constant")]; + fp16 const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = fp16(0x0p+0)]; + tensor input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = string("input_203_cast_fp16")]; + string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("valid")]; + int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1024)]; + tensor input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor([1])]; + tensor input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor([0, 0])]; + tensor input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor([1])]; + tensor const_254_to_fp16 = const()[name = string("const_254_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45565696)))]; + tensor const_255_to_fp16 = const()[name = string("const_255_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45584192)))]; + tensor input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16, x = input_203_cast_fp16)[name = string("input_207_cast_fp16")]; + tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = string("input_209_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("valid")]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45586304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45979584))))[name = string("module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_209_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = string("transpose_285")]; + tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + tensor input_215_axes_0 = const()[name = string("input_215_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45996032)))]; + tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45998144)))]; + tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46000256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47573184))))[name = string("module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_215_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47638784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49211712))))[name = string("module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_219_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)]; + tensor var_856_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_856_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor input_227_axes_0 = const()[name = string("input_227_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = string("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49228160)))]; + tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = string("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49230272)))]; + tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = string("input_227_cast_fp16")]; + tensor input_229_axes_0 = const()[name = string("input_229_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49232384)))]; + tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49234496)))]; + tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49236608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50809536))))[name = string("module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_229_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50875136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52448064))))[name = string("module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_233_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p-1)]; + tensor var_885_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_885_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52464512)))]; + tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52466624)))]; + tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = string("query_9_cast_fp16")]; + tensor module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52468736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52862016))))[name = string("module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_901 = const()[name = string("op_901"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_901, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52878464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53271744))))[name = string("module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_905 = const()[name = string("op_905"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_905, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53288192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53681472))))[name = string("module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_909 = const()[name = string("op_909"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_909, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53697920)))]; + tensor var_921_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_921_cast_fp16")]; + tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53700032)))]; + tensor var_923_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_923_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_95_transpose_x_0 = const()[name = string("x_95_transpose_x_0"), val = bool(false)]; + bool x_95_transpose_y_0 = const()[name = string("x_95_transpose_y_0"), val = bool(false)]; + tensor var_925_to_fp16 = const()[name = string("op_925_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53702144)))]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_923_cast_fp16)[name = string("transpose_284")]; + tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_925_to_fp16)[name = string("x_95_cast_fp16")]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_97_mode_0 = const()[name = string("x_97_mode_0"), val = string("constant")]; + fp16 const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = fp16(0x0p+0)]; + tensor x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_933 = const()[name = string("op_933"), val = tensor([1, 8, -1, 188])]; + tensor x_99_cast_fp16 = reshape(shape = var_933, x = x_97_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor var_937_begin_0 = const()[name = string("op_937_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_937_end_0 = const()[name = string("op_937_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_937_end_mask_0 = const()[name = string("op_937_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_937_cast_fp16 = slice_by_index(begin = var_937_begin_0, end = var_937_end_0, end_mask = var_937_end_mask_0, x = x_99_cast_fp16)[name = string("op_937_cast_fp16")]; + tensor var_938 = const()[name = string("op_938"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_938, x = var_937_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_921_cast_fp16)[name = string("transpose_283")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_947_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_947_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_947_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = string("scores_19_cast_fp16")]; + tensor var_953_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = string("op_953_cast_fp16")]; + tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_953_cast_fp16, cond = mask_3)[name = string("input_241_cast_fp16")]; + bool x_101_transpose_x_0 = const()[name = string("x_101_transpose_x_0"), val = bool(false)]; + bool x_101_transpose_y_0 = const()[name = string("x_101_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")]; + tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_957_perm_0 = const()[name = string("op_957_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_958 = const()[name = string("op_958"), val = tensor([1, -1, 1024])]; + tensor var_957_cast_fp16 = transpose(perm = var_957_perm_0, x = x_101_cast_fp16)[name = string("transpose_280")]; + tensor input_243_cast_fp16 = reshape(shape = var_958, x = var_957_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54470208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54863488))))[name = string("module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_243_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = string("input_247_cast_fp16")]; + tensor x_105_axes_0 = const()[name = string("x_105_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54879936)))]; + tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54882048)))]; + tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor input_249_perm_0 = const()[name = string("input_249_perm_0"), val = tensor([0, 2, 1])]; + string input_251_pad_type_0 = const()[name = string("input_251_pad_type_0"), val = string("valid")]; + tensor input_251_strides_0 = const()[name = string("input_251_strides_0"), val = tensor([1])]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0])]; + tensor input_251_dilations_0 = const()[name = string("input_251_dilations_0"), val = tensor([1])]; + int32 input_251_groups_0 = const()[name = string("input_251_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54884160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55670656))))[name = string("module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = string("transpose_279")]; + tensor input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + int32 x_107_split_num_splits_0 = const()[name = string("x_107_split_num_splits_0"), val = int32(2)]; + int32 x_107_split_axis_0 = const()[name = string("x_107_split_axis_0"), val = int32(1)]; + tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = string("x_107_split_cast_fp16")]; + tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = string("x_107_split_1_sigmoid_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_328)[name = string("input_253_cast_fp16")]; + tensor input_255_pad_0 = const()[name = string("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("constant")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = string("input_255_cast_fp16")]; + string input_257_pad_type_0 = const()[name = string("input_257_pad_type_0"), val = string("valid")]; + int32 input_257_groups_0 = const()[name = string("input_257_groups_0"), val = int32(1024)]; + tensor input_257_strides_0 = const()[name = string("input_257_strides_0"), val = tensor([1])]; + tensor input_257_pad_0 = const()[name = string("input_257_pad_0"), val = tensor([0, 0])]; + tensor input_257_dilations_0 = const()[name = string("input_257_dilations_0"), val = tensor([1])]; + tensor const_256_to_fp16 = const()[name = string("const_256_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55703488)))]; + tensor const_257_to_fp16 = const()[name = string("const_257_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55721984)))]; + tensor input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16, x = input_255_cast_fp16)[name = string("input_259_cast_fp16")]; + tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = string("input_261_cast_fp16")]; + string x_109_pad_type_0 = const()[name = string("x_109_pad_type_0"), val = string("valid")]; + tensor x_109_strides_0 = const()[name = string("x_109_strides_0"), val = tensor([1])]; + tensor x_109_pad_0 = const()[name = string("x_109_pad_0"), val = tensor([0, 0])]; + tensor x_109_dilations_0 = const()[name = string("x_109_dilations_0"), val = tensor([1])]; + int32 x_109_groups_0 = const()[name = string("x_109_groups_0"), val = int32(1)]; + tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55724096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56117376))))[name = string("module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_261_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = string("transpose_278")]; + tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor input_267_axes_0 = const()[name = string("input_267_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56133824)))]; + tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56135936)))]; + tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56138048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57710976))))[name = string("module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_267_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57776576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59349504))))[name = string("module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_271_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1019_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1019_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor input_279_axes_0 = const()[name = string("input_279_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = string("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59365952)))]; + tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = string("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59368064)))]; + tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = string("input_279_cast_fp16")]; + tensor input_281_axes_0 = const()[name = string("input_281_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59370176)))]; + tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59372288)))]; + tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = string("input_281_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59374400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60947328))))[name = string("module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_281_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61012928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62585856))))[name = string("module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_285_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1047_to_fp16 = const()[name = string("op_1047_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1048_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1047_to_fp16)[name = string("op_1048_cast_fp16")]; + tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1048_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62602304)))]; + tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62604416)))]; + tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = string("query_11_cast_fp16")]; + tensor module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62606528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62999808))))[name = string("module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1064 = const()[name = string("op_1064"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1064, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63016256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63409536))))[name = string("module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1068 = const()[name = string("op_1068"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1068, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63425984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63819264))))[name = string("module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1072 = const()[name = string("op_1072"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1072, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63835712)))]; + tensor var_1084_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1084_cast_fp16")]; + tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63837824)))]; + tensor var_1086_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1086_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63839936)))]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1086_cast_fp16)[name = string("transpose_277")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1088_to_fp16)[name = string("x_117_cast_fp16")]; + tensor x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor var_1096 = const()[name = string("op_1096"), val = tensor([1, 8, -1, 188])]; + tensor x_121_cast_fp16 = reshape(shape = var_1096, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor var_1100_begin_0 = const()[name = string("op_1100_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1100_end_0 = const()[name = string("op_1100_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1100_end_mask_0 = const()[name = string("op_1100_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1100_cast_fp16 = slice_by_index(begin = var_1100_begin_0, end = var_1100_end_0, end_mask = var_1100_end_mask_0, x = x_121_cast_fp16)[name = string("op_1100_cast_fp16")]; + tensor var_1101 = const()[name = string("op_1101"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1101, x = var_1100_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1084_cast_fp16)[name = string("transpose_276")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1110_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1110_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1110_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = string("scores_23_cast_fp16")]; + tensor var_1116_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = string("op_1116_cast_fp16")]; + tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1116_cast_fp16, cond = mask_3)[name = string("input_293_cast_fp16")]; + bool x_123_transpose_x_0 = const()[name = string("x_123_transpose_x_0"), val = bool(false)]; + bool x_123_transpose_y_0 = const()[name = string("x_123_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")]; + tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1120_perm_0 = const()[name = string("op_1120_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1121 = const()[name = string("op_1121"), val = tensor([1, -1, 1024])]; + tensor var_1120_cast_fp16 = transpose(perm = var_1120_perm_0, x = x_123_cast_fp16)[name = string("transpose_273")]; + tensor input_295_cast_fp16 = reshape(shape = var_1121, x = var_1120_cast_fp16)[name = string("input_295_cast_fp16")]; + tensor module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64608000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65001280))))[name = string("module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_295_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = string("input_299_cast_fp16")]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65017728)))]; + tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65019840)))]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_301_perm_0 = const()[name = string("input_301_perm_0"), val = tensor([0, 2, 1])]; + string input_303_pad_type_0 = const()[name = string("input_303_pad_type_0"), val = string("valid")]; + tensor input_303_strides_0 = const()[name = string("input_303_strides_0"), val = tensor([1])]; + tensor input_303_pad_0 = const()[name = string("input_303_pad_0"), val = tensor([0, 0])]; + tensor input_303_dilations_0 = const()[name = string("input_303_dilations_0"), val = tensor([1])]; + int32 input_303_groups_0 = const()[name = string("input_303_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65021952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65808448))))[name = string("module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = string("transpose_272")]; + tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_301_cast_fp16)[name = string("input_303_cast_fp16")]; + int32 x_129_split_num_splits_0 = const()[name = string("x_129_split_num_splits_0"), val = int32(2)]; + int32 x_129_split_axis_0 = const()[name = string("x_129_split_axis_0"), val = int32(1)]; + tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = string("x_129_split_cast_fp16")]; + tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = string("x_129_split_1_sigmoid_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_328)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("constant")]; + fp16 const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string input_309_pad_type_0 = const()[name = string("input_309_pad_type_0"), val = string("valid")]; + int32 input_309_groups_0 = const()[name = string("input_309_groups_0"), val = int32(1024)]; + tensor input_309_strides_0 = const()[name = string("input_309_strides_0"), val = tensor([1])]; + tensor input_309_pad_0 = const()[name = string("input_309_pad_0"), val = tensor([0, 0])]; + tensor input_309_dilations_0 = const()[name = string("input_309_dilations_0"), val = tensor([1])]; + tensor const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65841280)))]; + tensor const_259_to_fp16 = const()[name = string("const_259_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65859776)))]; + tensor input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16, x = input_307_cast_fp16)[name = string("input_311_cast_fp16")]; + tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = string("input_313_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("valid")]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([0, 0])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(1)]; + tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65861888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66255168))))[name = string("module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_313_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = string("transpose_271")]; + tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_axes_0 = const()[name = string("input_319_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66271616)))]; + tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66273728)))]; + tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66275840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67848768))))[name = string("module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_319_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67914368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69487296))))[name = string("module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_323_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1182_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1181_to_fp16)[name = string("op_1182_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1182_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_axes_0 = const()[name = string("input_331_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = string("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69503744)))]; + tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = string("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69505856)))]; + tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + tensor input_333_axes_0 = const()[name = string("input_333_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69507968)))]; + tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69510080)))]; + tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = string("input_333_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69512192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71085120))))[name = string("module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_333_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71150720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72723648))))[name = string("module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_337_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1210_to_fp16 = const()[name = string("op_1210_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1211_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1210_to_fp16)[name = string("op_1211_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1211_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72740096)))]; + tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72742208)))]; + tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = string("query_13_cast_fp16")]; + tensor module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72744320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73137600))))[name = string("module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1227 = const()[name = string("op_1227"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1227, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73154048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73547328))))[name = string("module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1231 = const()[name = string("op_1231"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1231, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73563776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73957056))))[name = string("module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1235 = const()[name = string("op_1235"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1235, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73973504)))]; + tensor var_1247_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1247_cast_fp16")]; + tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73975616)))]; + tensor var_1249_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1249_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_139_transpose_x_0 = const()[name = string("x_139_transpose_x_0"), val = bool(false)]; + bool x_139_transpose_y_0 = const()[name = string("x_139_transpose_y_0"), val = bool(false)]; + tensor var_1251_to_fp16 = const()[name = string("op_1251_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73977728)))]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1249_cast_fp16)[name = string("transpose_270")]; + tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1251_to_fp16)[name = string("x_139_cast_fp16")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_141_mode_0 = const()[name = string("x_141_mode_0"), val = string("constant")]; + fp16 const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = fp16(0x0p+0)]; + tensor x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1259 = const()[name = string("op_1259"), val = tensor([1, 8, -1, 188])]; + tensor x_143_cast_fp16 = reshape(shape = var_1259, x = x_141_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1263_begin_0 = const()[name = string("op_1263_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1263_end_0 = const()[name = string("op_1263_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1263_end_mask_0 = const()[name = string("op_1263_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1263_cast_fp16 = slice_by_index(begin = var_1263_begin_0, end = var_1263_end_0, end_mask = var_1263_end_mask_0, x = x_143_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor var_1264 = const()[name = string("op_1264"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1264, x = var_1263_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1247_cast_fp16)[name = string("transpose_269")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1273_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1273_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1273_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = string("scores_27_cast_fp16")]; + tensor var_1279_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = string("op_1279_cast_fp16")]; + tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1279_cast_fp16, cond = mask_3)[name = string("input_345_cast_fp16")]; + bool x_145_transpose_x_0 = const()[name = string("x_145_transpose_x_0"), val = bool(false)]; + bool x_145_transpose_y_0 = const()[name = string("x_145_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")]; + tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1283_perm_0 = const()[name = string("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1284 = const()[name = string("op_1284"), val = tensor([1, -1, 1024])]; + tensor var_1283_cast_fp16 = transpose(perm = var_1283_perm_0, x = x_145_cast_fp16)[name = string("transpose_266")]; + tensor input_347_cast_fp16 = reshape(shape = var_1284, x = var_1283_cast_fp16)[name = string("input_347_cast_fp16")]; + tensor module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74745792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75139072))))[name = string("module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_347_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = string("input_351_cast_fp16")]; + tensor x_149_axes_0 = const()[name = string("x_149_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75155520)))]; + tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75157632)))]; + tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + string input_355_pad_type_0 = const()[name = string("input_355_pad_type_0"), val = string("valid")]; + tensor input_355_strides_0 = const()[name = string("input_355_strides_0"), val = tensor([1])]; + tensor input_355_pad_0 = const()[name = string("input_355_pad_0"), val = tensor([0, 0])]; + tensor input_355_dilations_0 = const()[name = string("input_355_dilations_0"), val = tensor([1])]; + int32 input_355_groups_0 = const()[name = string("input_355_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75159744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75946240))))[name = string("module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = string("transpose_265")]; + tensor input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_353_cast_fp16)[name = string("input_355_cast_fp16")]; + int32 x_151_split_num_splits_0 = const()[name = string("x_151_split_num_splits_0"), val = int32(2)]; + int32 x_151_split_axis_0 = const()[name = string("x_151_split_axis_0"), val = int32(1)]; + tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = string("x_151_split_cast_fp16")]; + tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = string("x_151_split_1_sigmoid_cast_fp16")]; + tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_328)[name = string("input_357_cast_fp16")]; + tensor input_359_pad_0 = const()[name = string("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_359_mode_0 = const()[name = string("input_359_mode_0"), val = string("constant")]; + fp16 const_77_to_fp16 = const()[name = string("const_77_to_fp16"), val = fp16(0x0p+0)]; + tensor input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = string("input_359_cast_fp16")]; + string input_361_pad_type_0 = const()[name = string("input_361_pad_type_0"), val = string("valid")]; + int32 input_361_groups_0 = const()[name = string("input_361_groups_0"), val = int32(1024)]; + tensor input_361_strides_0 = const()[name = string("input_361_strides_0"), val = tensor([1])]; + tensor input_361_pad_0 = const()[name = string("input_361_pad_0"), val = tensor([0, 0])]; + tensor input_361_dilations_0 = const()[name = string("input_361_dilations_0"), val = tensor([1])]; + tensor const_260_to_fp16 = const()[name = string("const_260_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75979072)))]; + tensor const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75997568)))]; + tensor input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16, x = input_359_cast_fp16)[name = string("input_363_cast_fp16")]; + tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = string("input_365_cast_fp16")]; + string x_153_pad_type_0 = const()[name = string("x_153_pad_type_0"), val = string("valid")]; + tensor x_153_strides_0 = const()[name = string("x_153_strides_0"), val = tensor([1])]; + tensor x_153_pad_0 = const()[name = string("x_153_pad_0"), val = tensor([0, 0])]; + tensor x_153_dilations_0 = const()[name = string("x_153_dilations_0"), val = tensor([1])]; + int32 x_153_groups_0 = const()[name = string("x_153_groups_0"), val = int32(1)]; + tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75999680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76392960))))[name = string("module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_365_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = string("transpose_264")]; + tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + tensor input_371_axes_0 = const()[name = string("input_371_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76409408)))]; + tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76411520)))]; + tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76413632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77986560))))[name = string("module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_371_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78052160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79625088))))[name = string("module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_375_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1345_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1345_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor input_383_axes_0 = const()[name = string("input_383_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = string("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79641536)))]; + tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = string("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79643648)))]; + tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = string("input_383_cast_fp16")]; + tensor input_385_axes_0 = const()[name = string("input_385_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79645760)))]; + tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79647872)))]; + tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = string("input_385_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79649984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81222912))))[name = string("module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_385_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81288512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82861440))))[name = string("module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_389_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1373_to_fp16 = const()[name = string("op_1373_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1374_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1373_to_fp16)[name = string("op_1374_cast_fp16")]; + tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1374_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82877888)))]; + tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82880000)))]; + tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = string("query_15_cast_fp16")]; + tensor module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82882112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83275392))))[name = string("module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1390 = const()[name = string("op_1390"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1390, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83291840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83685120))))[name = string("module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1394 = const()[name = string("op_1394"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1394, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83701568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84094848))))[name = string("module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1398 = const()[name = string("op_1398"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1398, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84111296)))]; + tensor var_1410_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1410_cast_fp16")]; + tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84113408)))]; + tensor var_1412_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1412_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_161_transpose_x_0 = const()[name = string("x_161_transpose_x_0"), val = bool(false)]; + bool x_161_transpose_y_0 = const()[name = string("x_161_transpose_y_0"), val = bool(false)]; + tensor var_1414_to_fp16 = const()[name = string("op_1414_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84115520)))]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1412_cast_fp16)[name = string("transpose_263")]; + tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1414_to_fp16)[name = string("x_161_cast_fp16")]; + tensor x_163_pad_0 = const()[name = string("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_163_mode_0 = const()[name = string("x_163_mode_0"), val = string("constant")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor var_1422 = const()[name = string("op_1422"), val = tensor([1, 8, -1, 188])]; + tensor x_165_cast_fp16 = reshape(shape = var_1422, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1426_begin_0 = const()[name = string("op_1426_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1426_end_0 = const()[name = string("op_1426_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1426_end_mask_0 = const()[name = string("op_1426_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = x_165_cast_fp16)[name = string("op_1426_cast_fp16")]; + tensor var_1427 = const()[name = string("op_1427"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1427, x = var_1426_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1410_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_1436_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1436_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_1436_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = string("scores_31_cast_fp16")]; + tensor var_1442_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = string("op_1442_cast_fp16")]; + tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1442_cast_fp16, cond = mask_3)[name = string("input_397_cast_fp16")]; + bool x_167_transpose_x_0 = const()[name = string("x_167_transpose_x_0"), val = bool(false)]; + bool x_167_transpose_y_0 = const()[name = string("x_167_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")]; + tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1446_perm_0 = const()[name = string("op_1446_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1447 = const()[name = string("op_1447"), val = tensor([1, -1, 1024])]; + tensor var_1446_cast_fp16 = transpose(perm = var_1446_perm_0, x = x_167_cast_fp16)[name = string("transpose_259")]; + tensor input_399_cast_fp16 = reshape(shape = var_1447, x = var_1446_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84883584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85276864))))[name = string("module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_399_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = string("input_403_cast_fp16")]; + tensor x_171_axes_0 = const()[name = string("x_171_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85293312)))]; + tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85295424)))]; + tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string input_407_pad_type_0 = const()[name = string("input_407_pad_type_0"), val = string("valid")]; + tensor input_407_strides_0 = const()[name = string("input_407_strides_0"), val = tensor([1])]; + tensor input_407_pad_0 = const()[name = string("input_407_pad_0"), val = tensor([0, 0])]; + tensor input_407_dilations_0 = const()[name = string("input_407_dilations_0"), val = tensor([1])]; + int32 input_407_groups_0 = const()[name = string("input_407_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85297536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084032))))[name = string("module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = string("transpose_258")]; + tensor input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_405_cast_fp16)[name = string("input_407_cast_fp16")]; + int32 x_173_split_num_splits_0 = const()[name = string("x_173_split_num_splits_0"), val = int32(2)]; + int32 x_173_split_axis_0 = const()[name = string("x_173_split_axis_0"), val = int32(1)]; + tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = string("x_173_split_cast_fp16")]; + tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = string("x_173_split_1_sigmoid_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_328)[name = string("input_409_cast_fp16")]; + tensor input_411_pad_0 = const()[name = string("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_411_mode_0 = const()[name = string("input_411_mode_0"), val = string("constant")]; + fp16 const_87_to_fp16 = const()[name = string("const_87_to_fp16"), val = fp16(0x0p+0)]; + tensor input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = string("input_411_cast_fp16")]; + string input_413_pad_type_0 = const()[name = string("input_413_pad_type_0"), val = string("valid")]; + int32 input_413_groups_0 = const()[name = string("input_413_groups_0"), val = int32(1024)]; + tensor input_413_strides_0 = const()[name = string("input_413_strides_0"), val = tensor([1])]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0])]; + tensor input_413_dilations_0 = const()[name = string("input_413_dilations_0"), val = tensor([1])]; + tensor const_262_to_fp16 = const()[name = string("const_262_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86116864)))]; + tensor const_263_to_fp16 = const()[name = string("const_263_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86135360)))]; + tensor input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16, x = input_411_cast_fp16)[name = string("input_415_cast_fp16")]; + tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = string("input_417_cast_fp16")]; + string x_175_pad_type_0 = const()[name = string("x_175_pad_type_0"), val = string("valid")]; + tensor x_175_strides_0 = const()[name = string("x_175_strides_0"), val = tensor([1])]; + tensor x_175_pad_0 = const()[name = string("x_175_pad_0"), val = tensor([0, 0])]; + tensor x_175_dilations_0 = const()[name = string("x_175_dilations_0"), val = tensor([1])]; + int32 x_175_groups_0 = const()[name = string("x_175_groups_0"), val = int32(1)]; + tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86137472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86530752))))[name = string("module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_417_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = string("transpose_257")]; + tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = string("input_421_cast_fp16")]; + tensor input_423_axes_0 = const()[name = string("input_423_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86547200)))]; + tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86549312)))]; + tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86551424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88124352))))[name = string("module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_423_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88189952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89762880))))[name = string("module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_427_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1508_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1508_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor input_435_axes_0 = const()[name = string("input_435_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = string("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89779328)))]; + tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = string("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89781440)))]; + tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = string("input_435_cast_fp16")]; + tensor input_437_axes_0 = const()[name = string("input_437_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89783552)))]; + tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89785664)))]; + tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = string("input_437_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89787776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91360704))))[name = string("module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_437_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91426304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92999232))))[name = string("module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_441_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_1536_to_fp16 = const()[name = string("op_1536_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1537_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1536_to_fp16)[name = string("op_1537_cast_fp16")]; + tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1537_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93015680)))]; + tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93017792)))]; + tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = string("query_17_cast_fp16")]; + tensor module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93019904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93413184))))[name = string("module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_1553 = const()[name = string("op_1553"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_1553, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93429632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93822912))))[name = string("module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_1557 = const()[name = string("op_1557"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_1557, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93839360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94232640))))[name = string("module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_1561 = const()[name = string("op_1561"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_1561, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94249088)))]; + tensor var_1573_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1573_cast_fp16")]; + tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94251200)))]; + tensor var_1575_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1575_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_183_transpose_x_0 = const()[name = string("x_183_transpose_x_0"), val = bool(false)]; + bool x_183_transpose_y_0 = const()[name = string("x_183_transpose_y_0"), val = bool(false)]; + tensor var_1577_to_fp16 = const()[name = string("op_1577_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94253312)))]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1575_cast_fp16)[name = string("transpose_256")]; + tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1577_to_fp16)[name = string("x_183_cast_fp16")]; + tensor x_185_pad_0 = const()[name = string("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_185_mode_0 = const()[name = string("x_185_mode_0"), val = string("constant")]; + fp16 const_94_to_fp16 = const()[name = string("const_94_to_fp16"), val = fp16(0x0p+0)]; + tensor x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor var_1585 = const()[name = string("op_1585"), val = tensor([1, 8, -1, 188])]; + tensor x_187_cast_fp16 = reshape(shape = var_1585, x = x_185_cast_fp16)[name = string("x_187_cast_fp16")]; + tensor var_1589_begin_0 = const()[name = string("op_1589_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1589_end_0 = const()[name = string("op_1589_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1589_end_mask_0 = const()[name = string("op_1589_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1589_cast_fp16 = slice_by_index(begin = var_1589_begin_0, end = var_1589_end_0, end_mask = var_1589_end_mask_0, x = x_187_cast_fp16)[name = string("op_1589_cast_fp16")]; + tensor var_1590 = const()[name = string("op_1590"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1590, x = var_1589_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1573_cast_fp16)[name = string("transpose_255")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_1599_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1599_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_1599_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = string("scores_35_cast_fp16")]; + tensor var_1605_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = string("op_1605_cast_fp16")]; + tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1605_cast_fp16, cond = mask_3)[name = string("input_449_cast_fp16")]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = string("x_189_cast_fp16")]; + tensor var_1609_perm_0 = const()[name = string("op_1609_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1610 = const()[name = string("op_1610"), val = tensor([1, -1, 1024])]; + tensor var_1609_cast_fp16 = transpose(perm = var_1609_perm_0, x = x_189_cast_fp16)[name = string("transpose_252")]; + tensor input_451_cast_fp16 = reshape(shape = var_1610, x = var_1609_cast_fp16)[name = string("input_451_cast_fp16")]; + tensor module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95021376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95414656))))[name = string("module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_451_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = string("input_455_cast_fp16")]; + tensor x_193_axes_0 = const()[name = string("x_193_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95431104)))]; + tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95433216)))]; + tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor input_457_perm_0 = const()[name = string("input_457_perm_0"), val = tensor([0, 2, 1])]; + string input_459_pad_type_0 = const()[name = string("input_459_pad_type_0"), val = string("valid")]; + tensor input_459_strides_0 = const()[name = string("input_459_strides_0"), val = tensor([1])]; + tensor input_459_pad_0 = const()[name = string("input_459_pad_0"), val = tensor([0, 0])]; + tensor input_459_dilations_0 = const()[name = string("input_459_dilations_0"), val = tensor([1])]; + int32 input_459_groups_0 = const()[name = string("input_459_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95435328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96221824))))[name = string("module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = string("transpose_251")]; + tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_457_cast_fp16)[name = string("input_459_cast_fp16")]; + int32 x_195_split_num_splits_0 = const()[name = string("x_195_split_num_splits_0"), val = int32(2)]; + int32 x_195_split_axis_0 = const()[name = string("x_195_split_axis_0"), val = int32(1)]; + tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = string("x_195_split_cast_fp16")]; + tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = string("x_195_split_1_sigmoid_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_328)[name = string("input_461_cast_fp16")]; + tensor input_463_pad_0 = const()[name = string("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_463_mode_0 = const()[name = string("input_463_mode_0"), val = string("constant")]; + fp16 const_97_to_fp16 = const()[name = string("const_97_to_fp16"), val = fp16(0x0p+0)]; + tensor input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = string("input_463_cast_fp16")]; + string input_465_pad_type_0 = const()[name = string("input_465_pad_type_0"), val = string("valid")]; + int32 input_465_groups_0 = const()[name = string("input_465_groups_0"), val = int32(1024)]; + tensor input_465_strides_0 = const()[name = string("input_465_strides_0"), val = tensor([1])]; + tensor input_465_pad_0 = const()[name = string("input_465_pad_0"), val = tensor([0, 0])]; + tensor input_465_dilations_0 = const()[name = string("input_465_dilations_0"), val = tensor([1])]; + tensor const_264_to_fp16 = const()[name = string("const_264_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96254656)))]; + tensor const_265_to_fp16 = const()[name = string("const_265_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96273152)))]; + tensor input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16, x = input_463_cast_fp16)[name = string("input_467_cast_fp16")]; + tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = string("input_469_cast_fp16")]; + string x_197_pad_type_0 = const()[name = string("x_197_pad_type_0"), val = string("valid")]; + tensor x_197_strides_0 = const()[name = string("x_197_strides_0"), val = tensor([1])]; + tensor x_197_pad_0 = const()[name = string("x_197_pad_0"), val = tensor([0, 0])]; + tensor x_197_dilations_0 = const()[name = string("x_197_dilations_0"), val = tensor([1])]; + int32 x_197_groups_0 = const()[name = string("x_197_groups_0"), val = int32(1)]; + tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96275264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96668544))))[name = string("module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_469_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = string("transpose_250")]; + tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = string("input_473_cast_fp16")]; + tensor input_475_axes_0 = const()[name = string("input_475_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96684992)))]; + tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96687104)))]; + tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96689216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98262144))))[name = string("module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_475_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98327744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99900672))))[name = string("module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_479_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_1670_to_fp16 = const()[name = string("op_1670_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1671_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1670_to_fp16)[name = string("op_1671_cast_fp16")]; + tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1671_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor input_487_axes_0 = const()[name = string("input_487_axes_0"), val = tensor([-1])]; + tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = string("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99917120)))]; + tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = string("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99919232)))]; + tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = string("input_487_cast_fp16")]; + tensor input_489_axes_0 = const()[name = string("input_489_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99921344)))]; + tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99923456)))]; + tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = string("input_489_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99925568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101498496))))[name = string("module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_489_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101564096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103137024))))[name = string("module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_493_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_1699_to_fp16 = const()[name = string("op_1699_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1700_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1699_to_fp16)[name = string("op_1700_cast_fp16")]; + tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1700_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103153472)))]; + tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103155584)))]; + tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = string("query_19_cast_fp16")]; + tensor module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103157696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103550976))))[name = string("module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_1716 = const()[name = string("op_1716"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_1716, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103567424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103960704))))[name = string("module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_1720 = const()[name = string("op_1720"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_1720, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103977152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104370432))))[name = string("module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_1724 = const()[name = string("op_1724"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_1724, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104386880)))]; + tensor var_1736_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_1736_cast_fp16")]; + tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104388992)))]; + tensor var_1738_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_1738_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_205_transpose_x_0 = const()[name = string("x_205_transpose_x_0"), val = bool(false)]; + bool x_205_transpose_y_0 = const()[name = string("x_205_transpose_y_0"), val = bool(false)]; + tensor var_1740_to_fp16 = const()[name = string("op_1740_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104391104)))]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1738_cast_fp16)[name = string("transpose_249")]; + tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1740_to_fp16)[name = string("x_205_cast_fp16")]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_207_mode_0 = const()[name = string("x_207_mode_0"), val = string("constant")]; + fp16 const_104_to_fp16 = const()[name = string("const_104_to_fp16"), val = fp16(0x0p+0)]; + tensor x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor var_1748 = const()[name = string("op_1748"), val = tensor([1, 8, -1, 188])]; + tensor x_209_cast_fp16 = reshape(shape = var_1748, x = x_207_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor var_1752_begin_0 = const()[name = string("op_1752_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1752_end_0 = const()[name = string("op_1752_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1752_end_mask_0 = const()[name = string("op_1752_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1752_cast_fp16 = slice_by_index(begin = var_1752_begin_0, end = var_1752_end_0, end_mask = var_1752_end_mask_0, x = x_209_cast_fp16)[name = string("op_1752_cast_fp16")]; + tensor var_1753 = const()[name = string("op_1753"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1753, x = var_1752_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1736_cast_fp16)[name = string("transpose_248")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_1762_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_1762_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_1762_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = string("scores_39_cast_fp16")]; + tensor var_1768_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = string("op_1768_cast_fp16")]; + tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1768_cast_fp16, cond = mask_3)[name = string("input_501_cast_fp16")]; + bool x_211_transpose_x_0 = const()[name = string("x_211_transpose_x_0"), val = bool(false)]; + bool x_211_transpose_y_0 = const()[name = string("x_211_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")]; + tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_1772_perm_0 = const()[name = string("op_1772_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1773 = const()[name = string("op_1773"), val = tensor([1, -1, 1024])]; + tensor var_1772_cast_fp16 = transpose(perm = var_1772_perm_0, x = x_211_cast_fp16)[name = string("transpose_245")]; + tensor input_503_cast_fp16 = reshape(shape = var_1773, x = var_1772_cast_fp16)[name = string("input_503_cast_fp16")]; + tensor module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105159168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105552448))))[name = string("module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_503_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = string("input_507_cast_fp16")]; + tensor x_215_axes_0 = const()[name = string("x_215_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105568896)))]; + tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105571008)))]; + tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor input_509_perm_0 = const()[name = string("input_509_perm_0"), val = tensor([0, 2, 1])]; + string input_511_pad_type_0 = const()[name = string("input_511_pad_type_0"), val = string("valid")]; + tensor input_511_strides_0 = const()[name = string("input_511_strides_0"), val = tensor([1])]; + tensor input_511_pad_0 = const()[name = string("input_511_pad_0"), val = tensor([0, 0])]; + tensor input_511_dilations_0 = const()[name = string("input_511_dilations_0"), val = tensor([1])]; + int32 input_511_groups_0 = const()[name = string("input_511_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105573120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106359616))))[name = string("module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = string("transpose_244")]; + tensor input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_509_cast_fp16)[name = string("input_511_cast_fp16")]; + int32 x_217_split_num_splits_0 = const()[name = string("x_217_split_num_splits_0"), val = int32(2)]; + int32 x_217_split_axis_0 = const()[name = string("x_217_split_axis_0"), val = int32(1)]; + tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = string("x_217_split_cast_fp16")]; + tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = string("x_217_split_1_sigmoid_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_328)[name = string("input_513_cast_fp16")]; + tensor input_515_pad_0 = const()[name = string("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_515_mode_0 = const()[name = string("input_515_mode_0"), val = string("constant")]; + fp16 const_107_to_fp16 = const()[name = string("const_107_to_fp16"), val = fp16(0x0p+0)]; + tensor input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = string("input_515_cast_fp16")]; + string input_517_pad_type_0 = const()[name = string("input_517_pad_type_0"), val = string("valid")]; + int32 input_517_groups_0 = const()[name = string("input_517_groups_0"), val = int32(1024)]; + tensor input_517_strides_0 = const()[name = string("input_517_strides_0"), val = tensor([1])]; + tensor input_517_pad_0 = const()[name = string("input_517_pad_0"), val = tensor([0, 0])]; + tensor input_517_dilations_0 = const()[name = string("input_517_dilations_0"), val = tensor([1])]; + tensor const_266_to_fp16 = const()[name = string("const_266_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106392448)))]; + tensor const_267_to_fp16 = const()[name = string("const_267_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106410944)))]; + tensor input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16, x = input_515_cast_fp16)[name = string("input_519_cast_fp16")]; + tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = string("input_521_cast_fp16")]; + string x_219_pad_type_0 = const()[name = string("x_219_pad_type_0"), val = string("valid")]; + tensor x_219_strides_0 = const()[name = string("x_219_strides_0"), val = tensor([1])]; + tensor x_219_pad_0 = const()[name = string("x_219_pad_0"), val = tensor([0, 0])]; + tensor x_219_dilations_0 = const()[name = string("x_219_dilations_0"), val = tensor([1])]; + int32 x_219_groups_0 = const()[name = string("x_219_groups_0"), val = int32(1)]; + tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106413056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106806336))))[name = string("module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_521_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = string("transpose_243")]; + tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = string("input_525_cast_fp16")]; + tensor input_527_axes_0 = const()[name = string("input_527_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106822784)))]; + tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106824896)))]; + tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106827008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108399936))))[name = string("module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_527_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108465536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110038464))))[name = string("module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_531_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_1833_to_fp16 = const()[name = string("op_1833_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1834_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1833_to_fp16)[name = string("op_1834_cast_fp16")]; + tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1834_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor input_539_axes_0 = const()[name = string("input_539_axes_0"), val = tensor([-1])]; + tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = string("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110054912)))]; + tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = string("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110057024)))]; + tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = string("input_539_cast_fp16")]; + tensor input_541_axes_0 = const()[name = string("input_541_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110059136)))]; + tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110061248)))]; + tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = string("input_541_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110063360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111636288))))[name = string("module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_541_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111701888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113274816))))[name = string("module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_545_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_1862_to_fp16 = const()[name = string("op_1862_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1863_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1862_to_fp16)[name = string("op_1863_cast_fp16")]; + tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1863_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113291264)))]; + tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113293376)))]; + tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = string("query_21_cast_fp16")]; + tensor module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113295488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113688768))))[name = string("module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_1879 = const()[name = string("op_1879"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_1879, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113705216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114098496))))[name = string("module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_1883 = const()[name = string("op_1883"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_1883, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114114944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114508224))))[name = string("module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_1887 = const()[name = string("op_1887"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_1887, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114524672)))]; + tensor var_1899_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_1899_cast_fp16")]; + tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114526784)))]; + tensor var_1901_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_1901_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_227_transpose_x_0 = const()[name = string("x_227_transpose_x_0"), val = bool(false)]; + bool x_227_transpose_y_0 = const()[name = string("x_227_transpose_y_0"), val = bool(false)]; + tensor var_1903_to_fp16 = const()[name = string("op_1903_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114528896)))]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1901_cast_fp16)[name = string("transpose_242")]; + tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_1903_to_fp16)[name = string("x_227_cast_fp16")]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_229_mode_0 = const()[name = string("x_229_mode_0"), val = string("constant")]; + fp16 const_114_to_fp16 = const()[name = string("const_114_to_fp16"), val = fp16(0x0p+0)]; + tensor x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor var_1911 = const()[name = string("op_1911"), val = tensor([1, 8, -1, 188])]; + tensor x_231_cast_fp16 = reshape(shape = var_1911, x = x_229_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor var_1915_begin_0 = const()[name = string("op_1915_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1915_end_0 = const()[name = string("op_1915_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_1915_end_mask_0 = const()[name = string("op_1915_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1915_cast_fp16 = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = x_231_cast_fp16)[name = string("op_1915_cast_fp16")]; + tensor var_1916 = const()[name = string("op_1916"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_1916, x = var_1915_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1899_cast_fp16)[name = string("transpose_241")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_1925_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_1925_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_1925_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = string("scores_43_cast_fp16")]; + tensor var_1931_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = string("op_1931_cast_fp16")]; + tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_1931_cast_fp16, cond = mask_3)[name = string("input_553_cast_fp16")]; + bool x_233_transpose_x_0 = const()[name = string("x_233_transpose_x_0"), val = bool(false)]; + bool x_233_transpose_y_0 = const()[name = string("x_233_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")]; + tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor var_1935_perm_0 = const()[name = string("op_1935_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1936 = const()[name = string("op_1936"), val = tensor([1, -1, 1024])]; + tensor var_1935_cast_fp16 = transpose(perm = var_1935_perm_0, x = x_233_cast_fp16)[name = string("transpose_238")]; + tensor input_555_cast_fp16 = reshape(shape = var_1936, x = var_1935_cast_fp16)[name = string("input_555_cast_fp16")]; + tensor module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115296960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115690240))))[name = string("module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_555_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = string("input_559_cast_fp16")]; + tensor x_237_axes_0 = const()[name = string("x_237_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115706688)))]; + tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115708800)))]; + tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = string("x_237_cast_fp16")]; + tensor input_561_perm_0 = const()[name = string("input_561_perm_0"), val = tensor([0, 2, 1])]; + string input_563_pad_type_0 = const()[name = string("input_563_pad_type_0"), val = string("valid")]; + tensor input_563_strides_0 = const()[name = string("input_563_strides_0"), val = tensor([1])]; + tensor input_563_pad_0 = const()[name = string("input_563_pad_0"), val = tensor([0, 0])]; + tensor input_563_dilations_0 = const()[name = string("input_563_dilations_0"), val = tensor([1])]; + int32 input_563_groups_0 = const()[name = string("input_563_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115710912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116497408))))[name = string("module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = string("transpose_237")]; + tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_561_cast_fp16)[name = string("input_563_cast_fp16")]; + int32 x_239_split_num_splits_0 = const()[name = string("x_239_split_num_splits_0"), val = int32(2)]; + int32 x_239_split_axis_0 = const()[name = string("x_239_split_axis_0"), val = int32(1)]; + tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = string("x_239_split_cast_fp16")]; + tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = string("x_239_split_1_sigmoid_cast_fp16")]; + tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = string("x_239_cast_fp16")]; + tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_328)[name = string("input_565_cast_fp16")]; + tensor input_567_pad_0 = const()[name = string("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_567_mode_0 = const()[name = string("input_567_mode_0"), val = string("constant")]; + fp16 const_117_to_fp16 = const()[name = string("const_117_to_fp16"), val = fp16(0x0p+0)]; + tensor input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = string("input_567_cast_fp16")]; + string input_569_pad_type_0 = const()[name = string("input_569_pad_type_0"), val = string("valid")]; + int32 input_569_groups_0 = const()[name = string("input_569_groups_0"), val = int32(1024)]; + tensor input_569_strides_0 = const()[name = string("input_569_strides_0"), val = tensor([1])]; + tensor input_569_pad_0 = const()[name = string("input_569_pad_0"), val = tensor([0, 0])]; + tensor input_569_dilations_0 = const()[name = string("input_569_dilations_0"), val = tensor([1])]; + tensor const_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116530240)))]; + tensor const_269_to_fp16 = const()[name = string("const_269_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116548736)))]; + tensor input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16, x = input_567_cast_fp16)[name = string("input_571_cast_fp16")]; + tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = string("input_573_cast_fp16")]; + string x_241_pad_type_0 = const()[name = string("x_241_pad_type_0"), val = string("valid")]; + tensor x_241_strides_0 = const()[name = string("x_241_strides_0"), val = tensor([1])]; + tensor x_241_pad_0 = const()[name = string("x_241_pad_0"), val = tensor([0, 0])]; + tensor x_241_dilations_0 = const()[name = string("x_241_dilations_0"), val = tensor([1])]; + int32 x_241_groups_0 = const()[name = string("x_241_groups_0"), val = int32(1)]; + tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116550848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116944128))))[name = string("module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_573_cast_fp16)[name = string("x_241_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = string("transpose_236")]; + tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = string("input_577_cast_fp16")]; + tensor input_579_axes_0 = const()[name = string("input_579_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116960576)))]; + tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116962688)))]; + tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116964800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118537728))))[name = string("module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_579_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118603328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120176256))))[name = string("module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_583_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_1996_to_fp16 = const()[name = string("op_1996_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1997_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1996_to_fp16)[name = string("op_1997_cast_fp16")]; + tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1997_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor input_591_axes_0 = const()[name = string("input_591_axes_0"), val = tensor([-1])]; + tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = string("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120192704)))]; + tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = string("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120194816)))]; + tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = string("input_591_cast_fp16")]; + tensor input_593_axes_0 = const()[name = string("input_593_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120196928)))]; + tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120199040)))]; + tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = string("input_593_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120201152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121774080))))[name = string("module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_593_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121839680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123412608))))[name = string("module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_597_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2025_to_fp16 = const()[name = string("op_2025_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2026_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2025_to_fp16)[name = string("op_2026_cast_fp16")]; + tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2026_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123429056)))]; + tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123431168)))]; + tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = string("query_23_cast_fp16")]; + tensor module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123433280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123826560))))[name = string("module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2042 = const()[name = string("op_2042"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2042, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123843008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124236288))))[name = string("module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2046 = const()[name = string("op_2046"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2046, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124252736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124646016))))[name = string("module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2050 = const()[name = string("op_2050"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2050, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124662464)))]; + tensor var_2062_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2062_cast_fp16")]; + tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124664576)))]; + tensor var_2064_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2064_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_249_transpose_x_0 = const()[name = string("x_249_transpose_x_0"), val = bool(false)]; + bool x_249_transpose_y_0 = const()[name = string("x_249_transpose_y_0"), val = bool(false)]; + tensor var_2066_to_fp16 = const()[name = string("op_2066_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124666688)))]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2064_cast_fp16)[name = string("transpose_235")]; + tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2066_to_fp16)[name = string("x_249_cast_fp16")]; + tensor x_251_pad_0 = const()[name = string("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_251_mode_0 = const()[name = string("x_251_mode_0"), val = string("constant")]; + fp16 const_124_to_fp16 = const()[name = string("const_124_to_fp16"), val = fp16(0x0p+0)]; + tensor x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor var_2074 = const()[name = string("op_2074"), val = tensor([1, 8, -1, 188])]; + tensor x_253_cast_fp16 = reshape(shape = var_2074, x = x_251_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor var_2078_begin_0 = const()[name = string("op_2078_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2078_end_0 = const()[name = string("op_2078_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2078_end_mask_0 = const()[name = string("op_2078_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = x_253_cast_fp16)[name = string("op_2078_cast_fp16")]; + tensor var_2079 = const()[name = string("op_2079"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2079, x = var_2078_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2062_cast_fp16)[name = string("transpose_234")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2088_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2088_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2088_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = string("scores_47_cast_fp16")]; + tensor var_2094_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2094_cast_fp16, cond = mask_3)[name = string("input_605_cast_fp16")]; + bool x_255_transpose_x_0 = const()[name = string("x_255_transpose_x_0"), val = bool(false)]; + bool x_255_transpose_y_0 = const()[name = string("x_255_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")]; + tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2099 = const()[name = string("op_2099"), val = tensor([1, -1, 1024])]; + tensor var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = x_255_cast_fp16)[name = string("transpose_231")]; + tensor input_607_cast_fp16 = reshape(shape = var_2099, x = var_2098_cast_fp16)[name = string("input_607_cast_fp16")]; + tensor module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125434752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125828032))))[name = string("module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_607_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = string("input_611_cast_fp16")]; + tensor x_259_axes_0 = const()[name = string("x_259_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125844480)))]; + tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125846592)))]; + tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_613_perm_0 = const()[name = string("input_613_perm_0"), val = tensor([0, 2, 1])]; + string input_615_pad_type_0 = const()[name = string("input_615_pad_type_0"), val = string("valid")]; + tensor input_615_strides_0 = const()[name = string("input_615_strides_0"), val = tensor([1])]; + tensor input_615_pad_0 = const()[name = string("input_615_pad_0"), val = tensor([0, 0])]; + tensor input_615_dilations_0 = const()[name = string("input_615_dilations_0"), val = tensor([1])]; + int32 input_615_groups_0 = const()[name = string("input_615_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125848704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126635200))))[name = string("module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = string("transpose_230")]; + tensor input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_613_cast_fp16)[name = string("input_615_cast_fp16")]; + int32 x_261_split_num_splits_0 = const()[name = string("x_261_split_num_splits_0"), val = int32(2)]; + int32 x_261_split_axis_0 = const()[name = string("x_261_split_axis_0"), val = int32(1)]; + tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = string("x_261_split_cast_fp16")]; + tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = string("x_261_split_1_sigmoid_cast_fp16")]; + tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = string("x_261_cast_fp16")]; + tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_328)[name = string("input_617_cast_fp16")]; + tensor input_619_pad_0 = const()[name = string("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_619_mode_0 = const()[name = string("input_619_mode_0"), val = string("constant")]; + fp16 const_127_to_fp16 = const()[name = string("const_127_to_fp16"), val = fp16(0x0p+0)]; + tensor input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = string("input_619_cast_fp16")]; + string input_621_pad_type_0 = const()[name = string("input_621_pad_type_0"), val = string("valid")]; + int32 input_621_groups_0 = const()[name = string("input_621_groups_0"), val = int32(1024)]; + tensor input_621_strides_0 = const()[name = string("input_621_strides_0"), val = tensor([1])]; + tensor input_621_pad_0 = const()[name = string("input_621_pad_0"), val = tensor([0, 0])]; + tensor input_621_dilations_0 = const()[name = string("input_621_dilations_0"), val = tensor([1])]; + tensor const_270_to_fp16 = const()[name = string("const_270_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126668032)))]; + tensor const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126686528)))]; + tensor input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16, x = input_619_cast_fp16)[name = string("input_623_cast_fp16")]; + tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = string("input_625_cast_fp16")]; + string x_263_pad_type_0 = const()[name = string("x_263_pad_type_0"), val = string("valid")]; + tensor x_263_strides_0 = const()[name = string("x_263_strides_0"), val = tensor([1])]; + tensor x_263_pad_0 = const()[name = string("x_263_pad_0"), val = tensor([0, 0])]; + tensor x_263_dilations_0 = const()[name = string("x_263_dilations_0"), val = tensor([1])]; + int32 x_263_groups_0 = const()[name = string("x_263_groups_0"), val = int32(1)]; + tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126688640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127081920))))[name = string("module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_625_cast_fp16)[name = string("x_263_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = string("transpose_229")]; + tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = string("input_629_cast_fp16")]; + tensor input_631_axes_0 = const()[name = string("input_631_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127098368)))]; + tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127100480)))]; + tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127102592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128675520))))[name = string("module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_631_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128741120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130314048))))[name = string("module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_635_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2160_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2159_to_fp16)[name = string("op_2160_cast_fp16")]; + tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2160_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor input_643_axes_0 = const()[name = string("input_643_axes_0"), val = tensor([-1])]; + tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = string("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130330496)))]; + tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = string("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130332608)))]; + tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = string("input_643_cast_fp16")]; + tensor input_645_axes_0 = const()[name = string("input_645_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130334720)))]; + tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130336832)))]; + tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = string("input_645_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130338944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131911872))))[name = string("module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_645_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131977472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133550400))))[name = string("module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_649_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_2188_to_fp16 = const()[name = string("op_2188_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2189_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2188_to_fp16)[name = string("op_2189_cast_fp16")]; + tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2189_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133566848)))]; + tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133568960)))]; + tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = string("query_25_cast_fp16")]; + tensor module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133571072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133964352))))[name = string("module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_2205 = const()[name = string("op_2205"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_2205, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133980800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134374080))))[name = string("module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_2209 = const()[name = string("op_2209"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_2209, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134390528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134783808))))[name = string("module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_2213 = const()[name = string("op_2213"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_2213, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134800256)))]; + tensor var_2225_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2225_cast_fp16")]; + tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134802368)))]; + tensor var_2227_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2227_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_271_transpose_x_0 = const()[name = string("x_271_transpose_x_0"), val = bool(false)]; + bool x_271_transpose_y_0 = const()[name = string("x_271_transpose_y_0"), val = bool(false)]; + tensor var_2229_to_fp16 = const()[name = string("op_2229_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134804480)))]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2227_cast_fp16)[name = string("transpose_228")]; + tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2229_to_fp16)[name = string("x_271_cast_fp16")]; + tensor x_273_pad_0 = const()[name = string("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_273_mode_0 = const()[name = string("x_273_mode_0"), val = string("constant")]; + fp16 const_134_to_fp16 = const()[name = string("const_134_to_fp16"), val = fp16(0x0p+0)]; + tensor x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2237 = const()[name = string("op_2237"), val = tensor([1, 8, -1, 188])]; + tensor x_275_cast_fp16 = reshape(shape = var_2237, x = x_273_cast_fp16)[name = string("x_275_cast_fp16")]; + tensor var_2241_begin_0 = const()[name = string("op_2241_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2241_end_0 = const()[name = string("op_2241_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2241_end_mask_0 = const()[name = string("op_2241_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2241_cast_fp16 = slice_by_index(begin = var_2241_begin_0, end = var_2241_end_0, end_mask = var_2241_end_mask_0, x = x_275_cast_fp16)[name = string("op_2241_cast_fp16")]; + tensor var_2242 = const()[name = string("op_2242"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2242, x = var_2241_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2225_cast_fp16)[name = string("transpose_227")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_2251_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2251_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_2251_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = string("scores_51_cast_fp16")]; + tensor var_2257_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = string("op_2257_cast_fp16")]; + tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2257_cast_fp16, cond = mask_3)[name = string("input_657_cast_fp16")]; + bool x_277_transpose_x_0 = const()[name = string("x_277_transpose_x_0"), val = bool(false)]; + bool x_277_transpose_y_0 = const()[name = string("x_277_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")]; + tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor var_2261_perm_0 = const()[name = string("op_2261_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2262 = const()[name = string("op_2262"), val = tensor([1, -1, 1024])]; + tensor var_2261_cast_fp16 = transpose(perm = var_2261_perm_0, x = x_277_cast_fp16)[name = string("transpose_224")]; + tensor input_659_cast_fp16 = reshape(shape = var_2262, x = var_2261_cast_fp16)[name = string("input_659_cast_fp16")]; + tensor module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135572544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135965824))))[name = string("module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_659_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = string("input_663_cast_fp16")]; + tensor x_281_axes_0 = const()[name = string("x_281_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135982272)))]; + tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135984384)))]; + tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_665_perm_0 = const()[name = string("input_665_perm_0"), val = tensor([0, 2, 1])]; + string input_667_pad_type_0 = const()[name = string("input_667_pad_type_0"), val = string("valid")]; + tensor input_667_strides_0 = const()[name = string("input_667_strides_0"), val = tensor([1])]; + tensor input_667_pad_0 = const()[name = string("input_667_pad_0"), val = tensor([0, 0])]; + tensor input_667_dilations_0 = const()[name = string("input_667_dilations_0"), val = tensor([1])]; + int32 input_667_groups_0 = const()[name = string("input_667_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135986496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136772992))))[name = string("module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = string("transpose_223")]; + tensor input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_665_cast_fp16)[name = string("input_667_cast_fp16")]; + int32 x_283_split_num_splits_0 = const()[name = string("x_283_split_num_splits_0"), val = int32(2)]; + int32 x_283_split_axis_0 = const()[name = string("x_283_split_axis_0"), val = int32(1)]; + tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = string("x_283_split_cast_fp16")]; + tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = string("x_283_split_1_sigmoid_cast_fp16")]; + tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_328)[name = string("input_669_cast_fp16")]; + tensor input_671_pad_0 = const()[name = string("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_671_mode_0 = const()[name = string("input_671_mode_0"), val = string("constant")]; + fp16 const_137_to_fp16 = const()[name = string("const_137_to_fp16"), val = fp16(0x0p+0)]; + tensor input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = string("input_671_cast_fp16")]; + string input_673_pad_type_0 = const()[name = string("input_673_pad_type_0"), val = string("valid")]; + int32 input_673_groups_0 = const()[name = string("input_673_groups_0"), val = int32(1024)]; + tensor input_673_strides_0 = const()[name = string("input_673_strides_0"), val = tensor([1])]; + tensor input_673_pad_0 = const()[name = string("input_673_pad_0"), val = tensor([0, 0])]; + tensor input_673_dilations_0 = const()[name = string("input_673_dilations_0"), val = tensor([1])]; + tensor const_272_to_fp16 = const()[name = string("const_272_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136805824)))]; + tensor const_273_to_fp16 = const()[name = string("const_273_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136824320)))]; + tensor input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16, x = input_671_cast_fp16)[name = string("input_675_cast_fp16")]; + tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = string("input_677_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136826432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137219712))))[name = string("module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_677_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = string("transpose_222")]; + tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = string("input_681_cast_fp16")]; + tensor input_683_axes_0 = const()[name = string("input_683_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137236160)))]; + tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137238272)))]; + tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137240384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138813312))))[name = string("module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_683_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138878912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140451840))))[name = string("module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_687_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_2322_to_fp16 = const()[name = string("op_2322_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2323_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2322_to_fp16)[name = string("op_2323_cast_fp16")]; + tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2323_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor input_695_axes_0 = const()[name = string("input_695_axes_0"), val = tensor([-1])]; + tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = string("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140468288)))]; + tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = string("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140470400)))]; + tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = string("input_695_cast_fp16")]; + tensor input_697_axes_0 = const()[name = string("input_697_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140472512)))]; + tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140474624)))]; + tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = string("input_697_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140476736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142049664))))[name = string("module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_697_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142115264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143688192))))[name = string("module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_701_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_2351_to_fp16 = const()[name = string("op_2351_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2352_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2351_to_fp16)[name = string("op_2352_cast_fp16")]; + tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2352_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143704640)))]; + tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143706752)))]; + tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = string("query_27_cast_fp16")]; + tensor module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143708864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144102144))))[name = string("module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_2368 = const()[name = string("op_2368"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_2368, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144118592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144511872))))[name = string("module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_2372 = const()[name = string("op_2372"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_2372, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144528320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144921600))))[name = string("module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_2376 = const()[name = string("op_2376"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_2376, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144938048)))]; + tensor var_2388_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2388_cast_fp16")]; + tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144940160)))]; + tensor var_2390_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2390_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor var_2392_to_fp16 = const()[name = string("op_2392_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144942272)))]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2390_cast_fp16)[name = string("transpose_221")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2392_to_fp16)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2400 = const()[name = string("op_2400"), val = tensor([1, 8, -1, 188])]; + tensor x_297_cast_fp16 = reshape(shape = var_2400, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2404_begin_0 = const()[name = string("op_2404_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2404_end_0 = const()[name = string("op_2404_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2404_end_mask_0 = const()[name = string("op_2404_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2404_cast_fp16 = slice_by_index(begin = var_2404_begin_0, end = var_2404_end_0, end_mask = var_2404_end_mask_0, x = x_297_cast_fp16)[name = string("op_2404_cast_fp16")]; + tensor var_2405 = const()[name = string("op_2405"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2405, x = var_2404_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2388_cast_fp16)[name = string("transpose_220")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_2414_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2414_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_2414_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = string("scores_55_cast_fp16")]; + tensor var_2420_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = string("op_2420_cast_fp16")]; + tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2420_cast_fp16, cond = mask_3)[name = string("input_709_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2424_perm_0 = const()[name = string("op_2424_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2425 = const()[name = string("op_2425"), val = tensor([1, -1, 1024])]; + tensor var_2424_cast_fp16 = transpose(perm = var_2424_perm_0, x = x_299_cast_fp16)[name = string("transpose_217")]; + tensor input_711_cast_fp16 = reshape(shape = var_2425, x = var_2424_cast_fp16)[name = string("input_711_cast_fp16")]; + tensor module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145710336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146103616))))[name = string("module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_711_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = string("input_715_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146120064)))]; + tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146122176)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_717_perm_0 = const()[name = string("input_717_perm_0"), val = tensor([0, 2, 1])]; + string input_719_pad_type_0 = const()[name = string("input_719_pad_type_0"), val = string("valid")]; + tensor input_719_strides_0 = const()[name = string("input_719_strides_0"), val = tensor([1])]; + tensor input_719_pad_0 = const()[name = string("input_719_pad_0"), val = tensor([0, 0])]; + tensor input_719_dilations_0 = const()[name = string("input_719_dilations_0"), val = tensor([1])]; + int32 input_719_groups_0 = const()[name = string("input_719_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146124288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146910784))))[name = string("module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = string("transpose_216")]; + tensor input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_717_cast_fp16)[name = string("input_719_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_328)[name = string("input_721_cast_fp16")]; + tensor input_723_pad_0 = const()[name = string("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_723_mode_0 = const()[name = string("input_723_mode_0"), val = string("constant")]; + fp16 const_147_to_fp16 = const()[name = string("const_147_to_fp16"), val = fp16(0x0p+0)]; + tensor input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = string("input_723_cast_fp16")]; + string input_725_pad_type_0 = const()[name = string("input_725_pad_type_0"), val = string("valid")]; + int32 input_725_groups_0 = const()[name = string("input_725_groups_0"), val = int32(1024)]; + tensor input_725_strides_0 = const()[name = string("input_725_strides_0"), val = tensor([1])]; + tensor input_725_pad_0 = const()[name = string("input_725_pad_0"), val = tensor([0, 0])]; + tensor input_725_dilations_0 = const()[name = string("input_725_dilations_0"), val = tensor([1])]; + tensor const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146943616)))]; + tensor const_275_to_fp16 = const()[name = string("const_275_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146962112)))]; + tensor input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16, x = input_723_cast_fp16)[name = string("input_727_cast_fp16")]; + tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = string("input_729_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1)]; + tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146964224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147357504))))[name = string("module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_729_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = string("transpose_215")]; + tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = string("input_733_cast_fp16")]; + tensor input_735_axes_0 = const()[name = string("input_735_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147373952)))]; + tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147376064)))]; + tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147378176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148951104))))[name = string("module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_735_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149016704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150589632))))[name = string("module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_739_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_2485_to_fp16 = const()[name = string("op_2485_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2486_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2485_to_fp16)[name = string("op_2486_cast_fp16")]; + tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2486_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor input_747_axes_0 = const()[name = string("input_747_axes_0"), val = tensor([-1])]; + tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = string("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150606080)))]; + tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = string("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150608192)))]; + tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = string("input_747_cast_fp16")]; + tensor input_749_axes_0 = const()[name = string("input_749_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150610304)))]; + tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150612416)))]; + tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = string("input_749_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150614528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152187456))))[name = string("module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_749_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152253056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153825984))))[name = string("module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_753_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_2514_to_fp16 = const()[name = string("op_2514_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2515_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2514_to_fp16)[name = string("op_2515_cast_fp16")]; + tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2515_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153842432)))]; + tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153844544)))]; + tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = string("query_29_cast_fp16")]; + tensor module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153846656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154239936))))[name = string("module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_2531 = const()[name = string("op_2531"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_2531, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154256384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154649664))))[name = string("module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_2535 = const()[name = string("op_2535"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_2535, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154666112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155059392))))[name = string("module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_2539 = const()[name = string("op_2539"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_2539, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155075840)))]; + tensor var_2551_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2551_cast_fp16")]; + tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155077952)))]; + tensor var_2553_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2553_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_315_transpose_x_0 = const()[name = string("x_315_transpose_x_0"), val = bool(false)]; + bool x_315_transpose_y_0 = const()[name = string("x_315_transpose_y_0"), val = bool(false)]; + tensor var_2555_to_fp16 = const()[name = string("op_2555_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155080064)))]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2553_cast_fp16)[name = string("transpose_214")]; + tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2555_to_fp16)[name = string("x_315_cast_fp16")]; + tensor x_317_pad_0 = const()[name = string("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_317_mode_0 = const()[name = string("x_317_mode_0"), val = string("constant")]; + fp16 const_154_to_fp16 = const()[name = string("const_154_to_fp16"), val = fp16(0x0p+0)]; + tensor x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")]; + tensor var_2563 = const()[name = string("op_2563"), val = tensor([1, 8, -1, 188])]; + tensor x_319_cast_fp16 = reshape(shape = var_2563, x = x_317_cast_fp16)[name = string("x_319_cast_fp16")]; + tensor var_2567_begin_0 = const()[name = string("op_2567_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2567_end_0 = const()[name = string("op_2567_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2567_end_mask_0 = const()[name = string("op_2567_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2567_cast_fp16 = slice_by_index(begin = var_2567_begin_0, end = var_2567_end_0, end_mask = var_2567_end_mask_0, x = x_319_cast_fp16)[name = string("op_2567_cast_fp16")]; + tensor var_2568 = const()[name = string("op_2568"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2568, x = var_2567_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2551_cast_fp16)[name = string("transpose_213")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_2577_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_2577_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_2577_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = string("scores_59_cast_fp16")]; + tensor var_2583_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = string("op_2583_cast_fp16")]; + tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2583_cast_fp16, cond = mask_3)[name = string("input_761_cast_fp16")]; + bool x_321_transpose_x_0 = const()[name = string("x_321_transpose_x_0"), val = bool(false)]; + bool x_321_transpose_y_0 = const()[name = string("x_321_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")]; + tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_2587_perm_0 = const()[name = string("op_2587_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2588 = const()[name = string("op_2588"), val = tensor([1, -1, 1024])]; + tensor var_2587_cast_fp16 = transpose(perm = var_2587_perm_0, x = x_321_cast_fp16)[name = string("transpose_210")]; + tensor input_763_cast_fp16 = reshape(shape = var_2588, x = var_2587_cast_fp16)[name = string("input_763_cast_fp16")]; + tensor module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155848128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156241408))))[name = string("module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_763_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = string("input_767_cast_fp16")]; + tensor x_325_axes_0 = const()[name = string("x_325_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156257856)))]; + tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156259968)))]; + tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor input_769_perm_0 = const()[name = string("input_769_perm_0"), val = tensor([0, 2, 1])]; + string input_771_pad_type_0 = const()[name = string("input_771_pad_type_0"), val = string("valid")]; + tensor input_771_strides_0 = const()[name = string("input_771_strides_0"), val = tensor([1])]; + tensor input_771_pad_0 = const()[name = string("input_771_pad_0"), val = tensor([0, 0])]; + tensor input_771_dilations_0 = const()[name = string("input_771_dilations_0"), val = tensor([1])]; + int32 input_771_groups_0 = const()[name = string("input_771_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156262080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157048576))))[name = string("module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = string("transpose_209")]; + tensor input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_769_cast_fp16)[name = string("input_771_cast_fp16")]; + int32 x_327_split_num_splits_0 = const()[name = string("x_327_split_num_splits_0"), val = int32(2)]; + int32 x_327_split_axis_0 = const()[name = string("x_327_split_axis_0"), val = int32(1)]; + tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = string("x_327_split_cast_fp16")]; + tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = string("x_327_split_1_sigmoid_cast_fp16")]; + tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = string("x_327_cast_fp16")]; + tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_328)[name = string("input_773_cast_fp16")]; + tensor input_775_pad_0 = const()[name = string("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_775_mode_0 = const()[name = string("input_775_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = string("input_775_cast_fp16")]; + string input_777_pad_type_0 = const()[name = string("input_777_pad_type_0"), val = string("valid")]; + int32 input_777_groups_0 = const()[name = string("input_777_groups_0"), val = int32(1024)]; + tensor input_777_strides_0 = const()[name = string("input_777_strides_0"), val = tensor([1])]; + tensor input_777_pad_0 = const()[name = string("input_777_pad_0"), val = tensor([0, 0])]; + tensor input_777_dilations_0 = const()[name = string("input_777_dilations_0"), val = tensor([1])]; + tensor const_276_to_fp16 = const()[name = string("const_276_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157081408)))]; + tensor const_277_to_fp16 = const()[name = string("const_277_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157099904)))]; + tensor input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16, x = input_775_cast_fp16)[name = string("input_779_cast_fp16")]; + tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = string("input_781_cast_fp16")]; + string x_329_pad_type_0 = const()[name = string("x_329_pad_type_0"), val = string("valid")]; + tensor x_329_strides_0 = const()[name = string("x_329_strides_0"), val = tensor([1])]; + tensor x_329_pad_0 = const()[name = string("x_329_pad_0"), val = tensor([0, 0])]; + tensor x_329_dilations_0 = const()[name = string("x_329_dilations_0"), val = tensor([1])]; + int32 x_329_groups_0 = const()[name = string("x_329_groups_0"), val = int32(1)]; + tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157102016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157495296))))[name = string("module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_781_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = string("transpose_208")]; + tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = string("input_785_cast_fp16")]; + tensor input_787_axes_0 = const()[name = string("input_787_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157511744)))]; + tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157513856)))]; + tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157515968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159088896))))[name = string("module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_787_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159154496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160727424))))[name = string("module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_791_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_2648_to_fp16 = const()[name = string("op_2648_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2649_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2648_to_fp16)[name = string("op_2649_cast_fp16")]; + tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2649_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor input_799_axes_0 = const()[name = string("input_799_axes_0"), val = tensor([-1])]; + tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = string("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160743872)))]; + tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = string("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160745984)))]; + tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = string("input_799_cast_fp16")]; + tensor input_801_axes_0 = const()[name = string("input_801_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160748096)))]; + tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160750208)))]; + tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = string("input_801_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160752320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162325248))))[name = string("module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_801_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162390848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163963776))))[name = string("module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_805_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_2677_to_fp16 = const()[name = string("op_2677_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2678_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2677_to_fp16)[name = string("op_2678_cast_fp16")]; + tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2678_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163980224)))]; + tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163982336)))]; + tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = string("query_31_cast_fp16")]; + tensor module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163984448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164377728))))[name = string("module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_2694 = const()[name = string("op_2694"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_2694, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164394176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164787456))))[name = string("module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_2698 = const()[name = string("op_2698"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_2698, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164803904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165197184))))[name = string("module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_2702 = const()[name = string("op_2702"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_2702, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165213632)))]; + tensor var_2714_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_2714_cast_fp16")]; + tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165215744)))]; + tensor var_2716_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_2716_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_337_transpose_x_0 = const()[name = string("x_337_transpose_x_0"), val = bool(false)]; + bool x_337_transpose_y_0 = const()[name = string("x_337_transpose_y_0"), val = bool(false)]; + tensor var_2718_to_fp16 = const()[name = string("op_2718_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165217856)))]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2716_cast_fp16)[name = string("transpose_207")]; + tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_2718_to_fp16)[name = string("x_337_cast_fp16")]; + tensor x_339_pad_0 = const()[name = string("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_339_mode_0 = const()[name = string("x_339_mode_0"), val = string("constant")]; + fp16 const_164_to_fp16 = const()[name = string("const_164_to_fp16"), val = fp16(0x0p+0)]; + tensor x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")]; + tensor var_2726 = const()[name = string("op_2726"), val = tensor([1, 8, -1, 188])]; + tensor x_341_cast_fp16 = reshape(shape = var_2726, x = x_339_cast_fp16)[name = string("x_341_cast_fp16")]; + tensor var_2730_begin_0 = const()[name = string("op_2730_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2730_end_0 = const()[name = string("op_2730_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2730_end_mask_0 = const()[name = string("op_2730_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2730_cast_fp16 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_341_cast_fp16)[name = string("op_2730_cast_fp16")]; + tensor var_2731 = const()[name = string("op_2731"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_2731, x = var_2730_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2714_cast_fp16)[name = string("transpose_206")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_2740_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_2740_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_2740_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = string("scores_63_cast_fp16")]; + tensor var_2746_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = string("op_2746_cast_fp16")]; + tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2746_cast_fp16, cond = mask_3)[name = string("input_813_cast_fp16")]; + bool x_343_transpose_x_0 = const()[name = string("x_343_transpose_x_0"), val = bool(false)]; + bool x_343_transpose_y_0 = const()[name = string("x_343_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")]; + tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = string("x_343_cast_fp16")]; + tensor var_2750_perm_0 = const()[name = string("op_2750_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2751 = const()[name = string("op_2751"), val = tensor([1, -1, 1024])]; + tensor var_2750_cast_fp16 = transpose(perm = var_2750_perm_0, x = x_343_cast_fp16)[name = string("transpose_203")]; + tensor input_815_cast_fp16 = reshape(shape = var_2751, x = var_2750_cast_fp16)[name = string("input_815_cast_fp16")]; + tensor module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165985920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166379200))))[name = string("module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_815_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = string("input_819_cast_fp16")]; + tensor x_347_axes_0 = const()[name = string("x_347_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166395648)))]; + tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166397760)))]; + tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor input_821_perm_0 = const()[name = string("input_821_perm_0"), val = tensor([0, 2, 1])]; + string input_823_pad_type_0 = const()[name = string("input_823_pad_type_0"), val = string("valid")]; + tensor input_823_strides_0 = const()[name = string("input_823_strides_0"), val = tensor([1])]; + tensor input_823_pad_0 = const()[name = string("input_823_pad_0"), val = tensor([0, 0])]; + tensor input_823_dilations_0 = const()[name = string("input_823_dilations_0"), val = tensor([1])]; + int32 input_823_groups_0 = const()[name = string("input_823_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166399872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167186368))))[name = string("module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = string("transpose_202")]; + tensor input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_821_cast_fp16)[name = string("input_823_cast_fp16")]; + int32 x_349_split_num_splits_0 = const()[name = string("x_349_split_num_splits_0"), val = int32(2)]; + int32 x_349_split_axis_0 = const()[name = string("x_349_split_axis_0"), val = int32(1)]; + tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = string("x_349_split_cast_fp16")]; + tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = string("x_349_split_1_sigmoid_cast_fp16")]; + tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_328)[name = string("input_825_cast_fp16")]; + tensor input_827_pad_0 = const()[name = string("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_827_mode_0 = const()[name = string("input_827_mode_0"), val = string("constant")]; + fp16 const_167_to_fp16 = const()[name = string("const_167_to_fp16"), val = fp16(0x0p+0)]; + tensor input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = string("input_827_cast_fp16")]; + string input_829_pad_type_0 = const()[name = string("input_829_pad_type_0"), val = string("valid")]; + int32 input_829_groups_0 = const()[name = string("input_829_groups_0"), val = int32(1024)]; + tensor input_829_strides_0 = const()[name = string("input_829_strides_0"), val = tensor([1])]; + tensor input_829_pad_0 = const()[name = string("input_829_pad_0"), val = tensor([0, 0])]; + tensor input_829_dilations_0 = const()[name = string("input_829_dilations_0"), val = tensor([1])]; + tensor const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167219200)))]; + tensor const_279_to_fp16 = const()[name = string("const_279_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167237696)))]; + tensor input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16, x = input_827_cast_fp16)[name = string("input_831_cast_fp16")]; + tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = string("input_833_cast_fp16")]; + string x_351_pad_type_0 = const()[name = string("x_351_pad_type_0"), val = string("valid")]; + tensor x_351_strides_0 = const()[name = string("x_351_strides_0"), val = tensor([1])]; + tensor x_351_pad_0 = const()[name = string("x_351_pad_0"), val = tensor([0, 0])]; + tensor x_351_dilations_0 = const()[name = string("x_351_dilations_0"), val = tensor([1])]; + int32 x_351_groups_0 = const()[name = string("x_351_groups_0"), val = int32(1)]; + tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167239808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167633088))))[name = string("module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_833_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = string("transpose_201")]; + tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = string("input_837_cast_fp16")]; + tensor input_839_axes_0 = const()[name = string("input_839_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167649536)))]; + tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167651648)))]; + tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167653760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169226688))))[name = string("module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_839_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169292288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170865216))))[name = string("module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_843_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_2811_to_fp16 = const()[name = string("op_2811_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2812_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2811_to_fp16)[name = string("op_2812_cast_fp16")]; + tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2812_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor input_851_axes_0 = const()[name = string("input_851_axes_0"), val = tensor([-1])]; + tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = string("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170881664)))]; + tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = string("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170883776)))]; + tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = string("input_851_cast_fp16")]; + tensor input_853_axes_0 = const()[name = string("input_853_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170885888)))]; + tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170888000)))]; + tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = string("input_853_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170890112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172463040))))[name = string("module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_853_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172528640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174101568))))[name = string("module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_857_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_2840_to_fp16 = const()[name = string("op_2840_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2841_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2840_to_fp16)[name = string("op_2841_cast_fp16")]; + tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2841_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174118016)))]; + tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174120128)))]; + tensor query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = string("query_33_cast_fp16")]; + tensor module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174122240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174515520))))[name = string("module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_2857 = const()[name = string("op_2857"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_2857, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174531968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174925248))))[name = string("module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_2861 = const()[name = string("op_2861"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_2861, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174941696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175334976))))[name = string("module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_2865 = const()[name = string("op_2865"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_2865, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175351424)))]; + tensor var_2877_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_2877_cast_fp16")]; + tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175353536)))]; + tensor var_2879_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_2879_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_359_transpose_x_0 = const()[name = string("x_359_transpose_x_0"), val = bool(false)]; + bool x_359_transpose_y_0 = const()[name = string("x_359_transpose_y_0"), val = bool(false)]; + tensor var_2881_to_fp16 = const()[name = string("op_2881_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175355648)))]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2879_cast_fp16)[name = string("transpose_200")]; + tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = var_2881_to_fp16)[name = string("x_359_cast_fp16")]; + tensor x_361_pad_0 = const()[name = string("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_361_mode_0 = const()[name = string("x_361_mode_0"), val = string("constant")]; + fp16 const_174_to_fp16 = const()[name = string("const_174_to_fp16"), val = fp16(0x0p+0)]; + tensor x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor var_2889 = const()[name = string("op_2889"), val = tensor([1, 8, -1, 188])]; + tensor x_363_cast_fp16 = reshape(shape = var_2889, x = x_361_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor var_2893_begin_0 = const()[name = string("op_2893_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2893_end_0 = const()[name = string("op_2893_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_2893_end_mask_0 = const()[name = string("op_2893_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2893_cast_fp16 = slice_by_index(begin = var_2893_begin_0, end = var_2893_end_0, end_mask = var_2893_end_mask_0, x = x_363_cast_fp16)[name = string("op_2893_cast_fp16")]; + tensor var_2894 = const()[name = string("op_2894"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_2894, x = var_2893_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2877_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_2903_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_2903_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_2903_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = string("scores_67_cast_fp16")]; + tensor var_2909_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = string("op_2909_cast_fp16")]; + tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_2909_cast_fp16, cond = mask_3)[name = string("input_865_cast_fp16")]; + bool x_365_transpose_x_0 = const()[name = string("x_365_transpose_x_0"), val = bool(false)]; + bool x_365_transpose_y_0 = const()[name = string("x_365_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")]; + tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_35_cast_fp16)[name = string("x_365_cast_fp16")]; + tensor var_2913_perm_0 = const()[name = string("op_2913_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2914 = const()[name = string("op_2914"), val = tensor([1, -1, 1024])]; + tensor var_2913_cast_fp16 = transpose(perm = var_2913_perm_0, x = x_365_cast_fp16)[name = string("transpose_196")]; + tensor input_867_cast_fp16 = reshape(shape = var_2914, x = var_2913_cast_fp16)[name = string("input_867_cast_fp16")]; + tensor module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176123712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176516992))))[name = string("module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_867_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = string("input_871_cast_fp16")]; + tensor x_369_axes_0 = const()[name = string("x_369_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176533440)))]; + tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176535552)))]; + tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = string("x_369_cast_fp16")]; + tensor input_873_perm_0 = const()[name = string("input_873_perm_0"), val = tensor([0, 2, 1])]; + string input_875_pad_type_0 = const()[name = string("input_875_pad_type_0"), val = string("valid")]; + tensor input_875_strides_0 = const()[name = string("input_875_strides_0"), val = tensor([1])]; + tensor input_875_pad_0 = const()[name = string("input_875_pad_0"), val = tensor([0, 0])]; + tensor input_875_dilations_0 = const()[name = string("input_875_dilations_0"), val = tensor([1])]; + int32 input_875_groups_0 = const()[name = string("input_875_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176537664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177324160))))[name = string("module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = string("transpose_195")]; + tensor input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_873_cast_fp16)[name = string("input_875_cast_fp16")]; + int32 x_371_split_num_splits_0 = const()[name = string("x_371_split_num_splits_0"), val = int32(2)]; + int32 x_371_split_axis_0 = const()[name = string("x_371_split_axis_0"), val = int32(1)]; + tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = string("x_371_split_cast_fp16")]; + tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = string("x_371_split_1_sigmoid_cast_fp16")]; + tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = string("x_371_cast_fp16")]; + tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_328)[name = string("input_877_cast_fp16")]; + tensor input_879_pad_0 = const()[name = string("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_879_mode_0 = const()[name = string("input_879_mode_0"), val = string("constant")]; + fp16 const_177_to_fp16 = const()[name = string("const_177_to_fp16"), val = fp16(0x0p+0)]; + tensor input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = string("input_879_cast_fp16")]; + string input_881_pad_type_0 = const()[name = string("input_881_pad_type_0"), val = string("valid")]; + int32 input_881_groups_0 = const()[name = string("input_881_groups_0"), val = int32(1024)]; + tensor input_881_strides_0 = const()[name = string("input_881_strides_0"), val = tensor([1])]; + tensor input_881_pad_0 = const()[name = string("input_881_pad_0"), val = tensor([0, 0])]; + tensor input_881_dilations_0 = const()[name = string("input_881_dilations_0"), val = tensor([1])]; + tensor const_280_to_fp16 = const()[name = string("const_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177356992)))]; + tensor const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177375488)))]; + tensor input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16, x = input_879_cast_fp16)[name = string("input_883_cast_fp16")]; + tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = string("input_885_cast_fp16")]; + string x_373_pad_type_0 = const()[name = string("x_373_pad_type_0"), val = string("valid")]; + tensor x_373_strides_0 = const()[name = string("x_373_strides_0"), val = tensor([1])]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0])]; + tensor x_373_dilations_0 = const()[name = string("x_373_dilations_0"), val = tensor([1])]; + int32 x_373_groups_0 = const()[name = string("x_373_groups_0"), val = int32(1)]; + tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177377600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177770880))))[name = string("module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_885_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = string("transpose_194")]; + tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = string("input_889_cast_fp16")]; + tensor input_891_axes_0 = const()[name = string("input_891_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177787328)))]; + tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177789440)))]; + tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177791552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179364480))))[name = string("module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_891_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179430080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181003008))))[name = string("module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_895_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_2974_to_fp16 = const()[name = string("op_2974_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2975_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2974_to_fp16)[name = string("op_2975_cast_fp16")]; + tensor input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2975_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor input_903_axes_0 = const()[name = string("input_903_axes_0"), val = tensor([-1])]; + tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = string("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181019456)))]; + tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = string("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181021568)))]; + tensor input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = string("input_903_cast_fp16")]; + tensor input_905_axes_0 = const()[name = string("input_905_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181023680)))]; + tensor module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181025792)))]; + tensor input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = string("input_905_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181027904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182600832))))[name = string("module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_905_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182666432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184239360))))[name = string("module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_909_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_3003_to_fp16 = const()[name = string("op_3003_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3004_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3003_to_fp16)[name = string("op_3004_cast_fp16")]; + tensor input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3004_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184255808)))]; + tensor module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184257920)))]; + tensor query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = string("query_35_cast_fp16")]; + tensor module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184260032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184653312))))[name = string("module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_3020 = const()[name = string("op_3020"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_3020, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184669760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185063040))))[name = string("module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_3024 = const()[name = string("op_3024"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_3024, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185079488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185472768))))[name = string("module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_3028 = const()[name = string("op_3028"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_3028, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185489216)))]; + tensor var_3040_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3040_cast_fp16")]; + tensor module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185491328)))]; + tensor var_3042_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3042_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_381_transpose_x_0 = const()[name = string("x_381_transpose_x_0"), val = bool(false)]; + bool x_381_transpose_y_0 = const()[name = string("x_381_transpose_y_0"), val = bool(false)]; + tensor var_3044_to_fp16 = const()[name = string("op_3044_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185493440)))]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3042_cast_fp16)[name = string("transpose_193")]; + tensor x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = var_3044_to_fp16)[name = string("x_381_cast_fp16")]; + tensor x_383_pad_0 = const()[name = string("x_383_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_383_mode_0 = const()[name = string("x_383_mode_0"), val = string("constant")]; + fp16 const_184_to_fp16 = const()[name = string("const_184_to_fp16"), val = fp16(0x0p+0)]; + tensor x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor var_3052 = const()[name = string("op_3052"), val = tensor([1, 8, -1, 188])]; + tensor x_385_cast_fp16 = reshape(shape = var_3052, x = x_383_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor var_3056_begin_0 = const()[name = string("op_3056_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3056_end_0 = const()[name = string("op_3056_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3056_end_mask_0 = const()[name = string("op_3056_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3056_cast_fp16 = slice_by_index(begin = var_3056_begin_0, end = var_3056_end_0, end_mask = var_3056_end_mask_0, x = x_385_cast_fp16)[name = string("op_3056_cast_fp16")]; + tensor var_3057 = const()[name = string("op_3057"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3057, x = var_3056_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3040_cast_fp16)[name = string("transpose_192")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_3066_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3066_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_3066_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = string("scores_71_cast_fp16")]; + tensor var_3072_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = string("op_3072_cast_fp16")]; + tensor input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3072_cast_fp16, cond = mask_3)[name = string("input_917_cast_fp16")]; + bool x_387_transpose_x_0 = const()[name = string("x_387_transpose_x_0"), val = bool(false)]; + bool x_387_transpose_y_0 = const()[name = string("x_387_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")]; + tensor x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_37_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor var_3076_perm_0 = const()[name = string("op_3076_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3077 = const()[name = string("op_3077"), val = tensor([1, -1, 1024])]; + tensor var_3076_cast_fp16 = transpose(perm = var_3076_perm_0, x = x_387_cast_fp16)[name = string("transpose_189")]; + tensor input_919_cast_fp16 = reshape(shape = var_3077, x = var_3076_cast_fp16)[name = string("input_919_cast_fp16")]; + tensor module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186261504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186654784))))[name = string("module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_919_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = string("input_923_cast_fp16")]; + tensor x_391_axes_0 = const()[name = string("x_391_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("module_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186671232)))]; + tensor module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("module_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186673344)))]; + tensor x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = string("x_391_cast_fp16")]; + tensor input_925_perm_0 = const()[name = string("input_925_perm_0"), val = tensor([0, 2, 1])]; + string input_927_pad_type_0 = const()[name = string("input_927_pad_type_0"), val = string("valid")]; + tensor input_927_strides_0 = const()[name = string("input_927_strides_0"), val = tensor([1])]; + tensor input_927_pad_0 = const()[name = string("input_927_pad_0"), val = tensor([0, 0])]; + tensor input_927_dilations_0 = const()[name = string("input_927_dilations_0"), val = tensor([1])]; + int32 input_927_groups_0 = const()[name = string("input_927_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186675456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187461952))))[name = string("module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = string("transpose_188")]; + tensor input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_925_cast_fp16)[name = string("input_927_cast_fp16")]; + int32 x_393_split_num_splits_0 = const()[name = string("x_393_split_num_splits_0"), val = int32(2)]; + int32 x_393_split_axis_0 = const()[name = string("x_393_split_axis_0"), val = int32(1)]; + tensor x_393_split_cast_fp16_0, tensor x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = string("x_393_split_cast_fp16")]; + tensor x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = string("x_393_split_1_sigmoid_cast_fp16")]; + tensor x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = string("x_393_cast_fp16")]; + tensor input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_328)[name = string("input_929_cast_fp16")]; + tensor input_931_pad_0 = const()[name = string("input_931_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_931_mode_0 = const()[name = string("input_931_mode_0"), val = string("constant")]; + fp16 const_187_to_fp16 = const()[name = string("const_187_to_fp16"), val = fp16(0x0p+0)]; + tensor input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = string("input_931_cast_fp16")]; + string input_933_pad_type_0 = const()[name = string("input_933_pad_type_0"), val = string("valid")]; + int32 input_933_groups_0 = const()[name = string("input_933_groups_0"), val = int32(1024)]; + tensor input_933_strides_0 = const()[name = string("input_933_strides_0"), val = tensor([1])]; + tensor input_933_pad_0 = const()[name = string("input_933_pad_0"), val = tensor([0, 0])]; + tensor input_933_dilations_0 = const()[name = string("input_933_dilations_0"), val = tensor([1])]; + tensor const_282_to_fp16 = const()[name = string("const_282_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187494784)))]; + tensor const_283_to_fp16 = const()[name = string("const_283_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187513280)))]; + tensor input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16, x = input_931_cast_fp16)[name = string("input_935_cast_fp16")]; + tensor input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = string("input_937_cast_fp16")]; + string x_395_pad_type_0 = const()[name = string("x_395_pad_type_0"), val = string("valid")]; + tensor x_395_strides_0 = const()[name = string("x_395_strides_0"), val = tensor([1])]; + tensor x_395_pad_0 = const()[name = string("x_395_pad_0"), val = tensor([0, 0])]; + tensor x_395_dilations_0 = const()[name = string("x_395_dilations_0"), val = tensor([1])]; + int32 x_395_groups_0 = const()[name = string("x_395_groups_0"), val = int32(1)]; + tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187515392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187908672))))[name = string("module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = module_layers_17_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_937_cast_fp16)[name = string("x_395_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = string("transpose_187")]; + tensor input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = string("input_941_cast_fp16")]; + tensor input_943_axes_0 = const()[name = string("input_943_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187925120)))]; + tensor module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187927232)))]; + tensor input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187929344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189502272))))[name = string("module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_943_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189567872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191140800))))[name = string("module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_947_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_3137_to_fp16 = const()[name = string("op_3137_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3138_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3137_to_fp16)[name = string("op_3138_cast_fp16")]; + tensor input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3138_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor input_955_axes_0 = const()[name = string("input_955_axes_0"), val = tensor([-1])]; + tensor module_layers_17_norm_out_weight_to_fp16 = const()[name = string("module_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191157248)))]; + tensor module_layers_17_norm_out_bias_to_fp16 = const()[name = string("module_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191159360)))]; + tensor input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = string("input_955_cast_fp16")]; + tensor input_957_axes_0 = const()[name = string("input_957_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191161472)))]; + tensor module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191163584)))]; + tensor input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = string("input_957_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191165696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192738624))))[name = string("module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_957_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192804224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194377152))))[name = string("module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_961_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3167_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3166_to_fp16)[name = string("op_3167_cast_fp16")]; + tensor input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3167_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194393600)))]; + tensor module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194395712)))]; + tensor query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = string("query_37_cast_fp16")]; + tensor module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194397824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194791104))))[name = string("module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_3183 = const()[name = string("op_3183"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_3183, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194807552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195200832))))[name = string("module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_3187 = const()[name = string("op_3187"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_3187, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195217280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195610560))))[name = string("module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_3191 = const()[name = string("op_3191"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_3191, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195627008)))]; + tensor var_3203_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3203_cast_fp16")]; + tensor module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195629120)))]; + tensor var_3205_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3205_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor var_3207_to_fp16 = const()[name = string("op_3207_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195631232)))]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3205_cast_fp16)[name = string("transpose_186")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = var_3207_to_fp16)[name = string("x_403_cast_fp16")]; + tensor x_405_pad_0 = const()[name = string("x_405_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_405_mode_0 = const()[name = string("x_405_mode_0"), val = string("constant")]; + fp16 const_194_to_fp16 = const()[name = string("const_194_to_fp16"), val = fp16(0x0p+0)]; + tensor x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")]; + tensor var_3215 = const()[name = string("op_3215"), val = tensor([1, 8, -1, 188])]; + tensor x_407_cast_fp16 = reshape(shape = var_3215, x = x_405_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor var_3219_begin_0 = const()[name = string("op_3219_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3219_end_0 = const()[name = string("op_3219_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3219_end_mask_0 = const()[name = string("op_3219_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3219_cast_fp16 = slice_by_index(begin = var_3219_begin_0, end = var_3219_end_0, end_mask = var_3219_end_mask_0, x = x_407_cast_fp16)[name = string("op_3219_cast_fp16")]; + tensor var_3220 = const()[name = string("op_3220"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_3220, x = var_3219_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3203_cast_fp16)[name = string("transpose_185")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_3229_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3229_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_3229_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = string("scores_75_cast_fp16")]; + tensor var_3235_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = string("op_3235_cast_fp16")]; + tensor input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3235_cast_fp16, cond = mask_3)[name = string("input_969_cast_fp16")]; + bool x_409_transpose_x_0 = const()[name = string("x_409_transpose_x_0"), val = bool(false)]; + bool x_409_transpose_y_0 = const()[name = string("x_409_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")]; + tensor x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_39_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor var_3239_perm_0 = const()[name = string("op_3239_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3240 = const()[name = string("op_3240"), val = tensor([1, -1, 1024])]; + tensor var_3239_cast_fp16 = transpose(perm = var_3239_perm_0, x = x_409_cast_fp16)[name = string("transpose_182")]; + tensor input_971_cast_fp16 = reshape(shape = var_3240, x = var_3239_cast_fp16)[name = string("input_971_cast_fp16")]; + tensor module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196399296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196792576))))[name = string("module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_971_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = string("input_975_cast_fp16")]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("module_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196809024)))]; + tensor module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("module_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196811136)))]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_977_perm_0 = const()[name = string("input_977_perm_0"), val = tensor([0, 2, 1])]; + string input_979_pad_type_0 = const()[name = string("input_979_pad_type_0"), val = string("valid")]; + tensor input_979_strides_0 = const()[name = string("input_979_strides_0"), val = tensor([1])]; + tensor input_979_pad_0 = const()[name = string("input_979_pad_0"), val = tensor([0, 0])]; + tensor input_979_dilations_0 = const()[name = string("input_979_dilations_0"), val = tensor([1])]; + int32 input_979_groups_0 = const()[name = string("input_979_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196813248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197599744))))[name = string("module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = string("transpose_181")]; + tensor input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_977_cast_fp16)[name = string("input_979_cast_fp16")]; + int32 x_415_split_num_splits_0 = const()[name = string("x_415_split_num_splits_0"), val = int32(2)]; + int32 x_415_split_axis_0 = const()[name = string("x_415_split_axis_0"), val = int32(1)]; + tensor x_415_split_cast_fp16_0, tensor x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = string("x_415_split_cast_fp16")]; + tensor x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = string("x_415_split_1_sigmoid_cast_fp16")]; + tensor x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_328)[name = string("input_981_cast_fp16")]; + tensor input_983_pad_0 = const()[name = string("input_983_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_983_mode_0 = const()[name = string("input_983_mode_0"), val = string("constant")]; + fp16 const_197_to_fp16 = const()[name = string("const_197_to_fp16"), val = fp16(0x0p+0)]; + tensor input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = string("input_983_cast_fp16")]; + string input_985_pad_type_0 = const()[name = string("input_985_pad_type_0"), val = string("valid")]; + int32 input_985_groups_0 = const()[name = string("input_985_groups_0"), val = int32(1024)]; + tensor input_985_strides_0 = const()[name = string("input_985_strides_0"), val = tensor([1])]; + tensor input_985_pad_0 = const()[name = string("input_985_pad_0"), val = tensor([0, 0])]; + tensor input_985_dilations_0 = const()[name = string("input_985_dilations_0"), val = tensor([1])]; + tensor const_284_to_fp16 = const()[name = string("const_284_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197632576)))]; + tensor const_285_to_fp16 = const()[name = string("const_285_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197651072)))]; + tensor input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16, x = input_983_cast_fp16)[name = string("input_987_cast_fp16")]; + tensor input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = string("input_989_cast_fp16")]; + string x_417_pad_type_0 = const()[name = string("x_417_pad_type_0"), val = string("valid")]; + tensor x_417_strides_0 = const()[name = string("x_417_strides_0"), val = tensor([1])]; + tensor x_417_pad_0 = const()[name = string("x_417_pad_0"), val = tensor([0, 0])]; + tensor x_417_dilations_0 = const()[name = string("x_417_dilations_0"), val = tensor([1])]; + int32 x_417_groups_0 = const()[name = string("x_417_groups_0"), val = int32(1)]; + tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197653184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198046464))))[name = string("module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = module_layers_18_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_989_cast_fp16)[name = string("x_417_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = string("transpose_180")]; + tensor input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = string("input_993_cast_fp16")]; + tensor input_995_axes_0 = const()[name = string("input_995_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198062912)))]; + tensor module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198065024)))]; + tensor input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198067136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199640064))))[name = string("module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_995_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199705664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201278592))))[name = string("module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_999_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_3300_to_fp16 = const()[name = string("op_3300_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3301_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3300_to_fp16)[name = string("op_3301_cast_fp16")]; + tensor input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3301_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor input_1007_axes_0 = const()[name = string("input_1007_axes_0"), val = tensor([-1])]; + tensor module_layers_18_norm_out_weight_to_fp16 = const()[name = string("module_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201295040)))]; + tensor module_layers_18_norm_out_bias_to_fp16 = const()[name = string("module_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201297152)))]; + tensor input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = string("input_1007_cast_fp16")]; + tensor input_1009_axes_0 = const()[name = string("input_1009_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201299264)))]; + tensor module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201301376)))]; + tensor input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = string("input_1009_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201303488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202876416))))[name = string("module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1009_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202942016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204514944))))[name = string("module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1013_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_3329_to_fp16 = const()[name = string("op_3329_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3330_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3329_to_fp16)[name = string("op_3330_cast_fp16")]; + tensor input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3330_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204531392)))]; + tensor module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204533504)))]; + tensor query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = string("query_39_cast_fp16")]; + tensor module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204535616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204928896))))[name = string("module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_3346 = const()[name = string("op_3346"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_3346, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204945344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205338624))))[name = string("module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_3350 = const()[name = string("op_3350"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_3350, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205355072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205748352))))[name = string("module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_3354 = const()[name = string("op_3354"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_3354, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205764800)))]; + tensor var_3366_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3366_cast_fp16")]; + tensor module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205766912)))]; + tensor var_3368_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3368_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_425_transpose_x_0 = const()[name = string("x_425_transpose_x_0"), val = bool(false)]; + bool x_425_transpose_y_0 = const()[name = string("x_425_transpose_y_0"), val = bool(false)]; + tensor var_3370_to_fp16 = const()[name = string("op_3370_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205769024)))]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3368_cast_fp16)[name = string("transpose_179")]; + tensor x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = var_3370_to_fp16)[name = string("x_425_cast_fp16")]; + tensor x_427_pad_0 = const()[name = string("x_427_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_427_mode_0 = const()[name = string("x_427_mode_0"), val = string("constant")]; + fp16 const_204_to_fp16 = const()[name = string("const_204_to_fp16"), val = fp16(0x0p+0)]; + tensor x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3378 = const()[name = string("op_3378"), val = tensor([1, 8, -1, 188])]; + tensor x_429_cast_fp16 = reshape(shape = var_3378, x = x_427_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3382_begin_0 = const()[name = string("op_3382_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3382_end_0 = const()[name = string("op_3382_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3382_end_mask_0 = const()[name = string("op_3382_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3382_cast_fp16 = slice_by_index(begin = var_3382_begin_0, end = var_3382_end_0, end_mask = var_3382_end_mask_0, x = x_429_cast_fp16)[name = string("op_3382_cast_fp16")]; + tensor var_3383 = const()[name = string("op_3383"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3383, x = var_3382_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3366_cast_fp16)[name = string("transpose_178")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_3392_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3392_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_3392_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = string("scores_79_cast_fp16")]; + tensor var_3398_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = string("op_3398_cast_fp16")]; + tensor input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3398_cast_fp16, cond = mask_3)[name = string("input_1021_cast_fp16")]; + bool x_431_transpose_x_0 = const()[name = string("x_431_transpose_x_0"), val = bool(false)]; + bool x_431_transpose_y_0 = const()[name = string("x_431_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")]; + tensor x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_41_cast_fp16)[name = string("x_431_cast_fp16")]; + tensor var_3402_perm_0 = const()[name = string("op_3402_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3403 = const()[name = string("op_3403"), val = tensor([1, -1, 1024])]; + tensor var_3402_cast_fp16 = transpose(perm = var_3402_perm_0, x = x_431_cast_fp16)[name = string("transpose_175")]; + tensor input_1023_cast_fp16 = reshape(shape = var_3403, x = var_3402_cast_fp16)[name = string("input_1023_cast_fp16")]; + tensor module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206537088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206930368))))[name = string("module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1023_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1027_cast_fp16")]; + tensor x_435_axes_0 = const()[name = string("x_435_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("module_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206946816)))]; + tensor module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("module_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206948928)))]; + tensor x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_1029_perm_0 = const()[name = string("input_1029_perm_0"), val = tensor([0, 2, 1])]; + string input_1031_pad_type_0 = const()[name = string("input_1031_pad_type_0"), val = string("valid")]; + tensor input_1031_strides_0 = const()[name = string("input_1031_strides_0"), val = tensor([1])]; + tensor input_1031_pad_0 = const()[name = string("input_1031_pad_0"), val = tensor([0, 0])]; + tensor input_1031_dilations_0 = const()[name = string("input_1031_dilations_0"), val = tensor([1])]; + int32 input_1031_groups_0 = const()[name = string("input_1031_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206951040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207737536))))[name = string("module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = string("transpose_174")]; + tensor input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1029_cast_fp16)[name = string("input_1031_cast_fp16")]; + int32 x_437_split_num_splits_0 = const()[name = string("x_437_split_num_splits_0"), val = int32(2)]; + int32 x_437_split_axis_0 = const()[name = string("x_437_split_axis_0"), val = int32(1)]; + tensor x_437_split_cast_fp16_0, tensor x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = string("x_437_split_cast_fp16")]; + tensor x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = string("x_437_split_1_sigmoid_cast_fp16")]; + tensor x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_328)[name = string("input_1033_cast_fp16")]; + tensor input_1035_pad_0 = const()[name = string("input_1035_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1035_mode_0 = const()[name = string("input_1035_mode_0"), val = string("constant")]; + fp16 const_207_to_fp16 = const()[name = string("const_207_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = string("input_1035_cast_fp16")]; + string input_1037_pad_type_0 = const()[name = string("input_1037_pad_type_0"), val = string("valid")]; + int32 input_1037_groups_0 = const()[name = string("input_1037_groups_0"), val = int32(1024)]; + tensor input_1037_strides_0 = const()[name = string("input_1037_strides_0"), val = tensor([1])]; + tensor input_1037_pad_0 = const()[name = string("input_1037_pad_0"), val = tensor([0, 0])]; + tensor input_1037_dilations_0 = const()[name = string("input_1037_dilations_0"), val = tensor([1])]; + tensor const_286_to_fp16 = const()[name = string("const_286_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207770368)))]; + tensor const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207788864)))]; + tensor input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16, x = input_1035_cast_fp16)[name = string("input_1039_cast_fp16")]; + tensor input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = string("input_1041_cast_fp16")]; + string x_439_pad_type_0 = const()[name = string("x_439_pad_type_0"), val = string("valid")]; + tensor x_439_strides_0 = const()[name = string("x_439_strides_0"), val = tensor([1])]; + tensor x_439_pad_0 = const()[name = string("x_439_pad_0"), val = tensor([0, 0])]; + tensor x_439_dilations_0 = const()[name = string("x_439_dilations_0"), val = tensor([1])]; + int32 x_439_groups_0 = const()[name = string("x_439_groups_0"), val = int32(1)]; + tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207790976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208184256))))[name = string("module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = module_layers_19_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1041_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = string("transpose_173")]; + tensor input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = string("input_1045_cast_fp16")]; + tensor input_1047_axes_0 = const()[name = string("input_1047_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208200704)))]; + tensor module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208202816)))]; + tensor input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208204928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209777856))))[name = string("module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1047_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209843456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211416384))))[name = string("module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1051_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_3463_to_fp16 = const()[name = string("op_3463_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3464_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3463_to_fp16)[name = string("op_3464_cast_fp16")]; + tensor input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3464_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor input_1059_axes_0 = const()[name = string("input_1059_axes_0"), val = tensor([-1])]; + tensor module_layers_19_norm_out_weight_to_fp16 = const()[name = string("module_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211432832)))]; + tensor module_layers_19_norm_out_bias_to_fp16 = const()[name = string("module_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211434944)))]; + tensor input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = string("input_1059_cast_fp16")]; + tensor input_1061_axes_0 = const()[name = string("input_1061_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211437056)))]; + tensor module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211439168)))]; + tensor input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = string("input_1061_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211441280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213014208))))[name = string("module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1061_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213079808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214652736))))[name = string("module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1065_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_3492_to_fp16 = const()[name = string("op_3492_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3493_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3492_to_fp16)[name = string("op_3493_cast_fp16")]; + tensor input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3493_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214669184)))]; + tensor module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214671296)))]; + tensor query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = string("query_41_cast_fp16")]; + tensor module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214673408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215066688))))[name = string("module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_3509 = const()[name = string("op_3509"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_3509, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215083136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215476416))))[name = string("module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_3513 = const()[name = string("op_3513"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_3513, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215492864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215886144))))[name = string("module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_3517 = const()[name = string("op_3517"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_3517, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215902592)))]; + tensor var_3529_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_3529_cast_fp16")]; + tensor module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215904704)))]; + tensor var_3531_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_3531_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_447_transpose_x_0 = const()[name = string("x_447_transpose_x_0"), val = bool(false)]; + bool x_447_transpose_y_0 = const()[name = string("x_447_transpose_y_0"), val = bool(false)]; + tensor var_3533_to_fp16 = const()[name = string("op_3533_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215906816)))]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3531_cast_fp16)[name = string("transpose_172")]; + tensor x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = var_3533_to_fp16)[name = string("x_447_cast_fp16")]; + tensor x_449_pad_0 = const()[name = string("x_449_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_449_mode_0 = const()[name = string("x_449_mode_0"), val = string("constant")]; + fp16 const_214_to_fp16 = const()[name = string("const_214_to_fp16"), val = fp16(0x0p+0)]; + tensor x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")]; + tensor var_3541 = const()[name = string("op_3541"), val = tensor([1, 8, -1, 188])]; + tensor x_451_cast_fp16 = reshape(shape = var_3541, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_3545_begin_0 = const()[name = string("op_3545_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3545_end_0 = const()[name = string("op_3545_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3545_end_mask_0 = const()[name = string("op_3545_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3545_cast_fp16 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = x_451_cast_fp16)[name = string("op_3545_cast_fp16")]; + tensor var_3546 = const()[name = string("op_3546"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_3546, x = var_3545_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3529_cast_fp16)[name = string("transpose_171")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_3555_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_3555_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_3555_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = string("scores_83_cast_fp16")]; + tensor var_3561_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = string("op_3561_cast_fp16")]; + tensor input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3561_cast_fp16, cond = mask_3)[name = string("input_1073_cast_fp16")]; + bool x_453_transpose_x_0 = const()[name = string("x_453_transpose_x_0"), val = bool(false)]; + bool x_453_transpose_y_0 = const()[name = string("x_453_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")]; + tensor x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_43_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_3565_perm_0 = const()[name = string("op_3565_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3566 = const()[name = string("op_3566"), val = tensor([1, -1, 1024])]; + tensor var_3565_cast_fp16 = transpose(perm = var_3565_perm_0, x = x_453_cast_fp16)[name = string("transpose_168")]; + tensor input_1075_cast_fp16 = reshape(shape = var_3566, x = var_3565_cast_fp16)[name = string("input_1075_cast_fp16")]; + tensor module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216674880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217068160))))[name = string("module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1075_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1079_cast_fp16")]; + tensor x_457_axes_0 = const()[name = string("x_457_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("module_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217084608)))]; + tensor module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("module_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217086720)))]; + tensor x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = string("x_457_cast_fp16")]; + tensor input_1081_perm_0 = const()[name = string("input_1081_perm_0"), val = tensor([0, 2, 1])]; + string input_1083_pad_type_0 = const()[name = string("input_1083_pad_type_0"), val = string("valid")]; + tensor input_1083_strides_0 = const()[name = string("input_1083_strides_0"), val = tensor([1])]; + tensor input_1083_pad_0 = const()[name = string("input_1083_pad_0"), val = tensor([0, 0])]; + tensor input_1083_dilations_0 = const()[name = string("input_1083_dilations_0"), val = tensor([1])]; + int32 input_1083_groups_0 = const()[name = string("input_1083_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217088832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217875328))))[name = string("module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = string("transpose_167")]; + tensor input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1081_cast_fp16)[name = string("input_1083_cast_fp16")]; + int32 x_459_split_num_splits_0 = const()[name = string("x_459_split_num_splits_0"), val = int32(2)]; + int32 x_459_split_axis_0 = const()[name = string("x_459_split_axis_0"), val = int32(1)]; + tensor x_459_split_cast_fp16_0, tensor x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = string("x_459_split_cast_fp16")]; + tensor x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = string("x_459_split_1_sigmoid_cast_fp16")]; + tensor x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_328)[name = string("input_1085_cast_fp16")]; + tensor input_1087_pad_0 = const()[name = string("input_1087_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1087_mode_0 = const()[name = string("input_1087_mode_0"), val = string("constant")]; + fp16 const_217_to_fp16 = const()[name = string("const_217_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = string("input_1087_cast_fp16")]; + string input_1089_pad_type_0 = const()[name = string("input_1089_pad_type_0"), val = string("valid")]; + int32 input_1089_groups_0 = const()[name = string("input_1089_groups_0"), val = int32(1024)]; + tensor input_1089_strides_0 = const()[name = string("input_1089_strides_0"), val = tensor([1])]; + tensor input_1089_pad_0 = const()[name = string("input_1089_pad_0"), val = tensor([0, 0])]; + tensor input_1089_dilations_0 = const()[name = string("input_1089_dilations_0"), val = tensor([1])]; + tensor const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217908160)))]; + tensor const_289_to_fp16 = const()[name = string("const_289_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217926656)))]; + tensor input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16, x = input_1087_cast_fp16)[name = string("input_1091_cast_fp16")]; + tensor input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = string("input_1093_cast_fp16")]; + string x_461_pad_type_0 = const()[name = string("x_461_pad_type_0"), val = string("valid")]; + tensor x_461_strides_0 = const()[name = string("x_461_strides_0"), val = tensor([1])]; + tensor x_461_pad_0 = const()[name = string("x_461_pad_0"), val = tensor([0, 0])]; + tensor x_461_dilations_0 = const()[name = string("x_461_dilations_0"), val = tensor([1])]; + int32 x_461_groups_0 = const()[name = string("x_461_groups_0"), val = int32(1)]; + tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217928768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218322048))))[name = string("module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = module_layers_20_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1093_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = string("transpose_166")]; + tensor input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = string("input_1097_cast_fp16")]; + tensor input_1099_axes_0 = const()[name = string("input_1099_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218338496)))]; + tensor module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218340608)))]; + tensor input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218342720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219915648))))[name = string("module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1099_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219981248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221554176))))[name = string("module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1103_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_3626_to_fp16 = const()[name = string("op_3626_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3627_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3626_to_fp16)[name = string("op_3627_cast_fp16")]; + tensor input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3627_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor input_1111_axes_0 = const()[name = string("input_1111_axes_0"), val = tensor([-1])]; + tensor module_layers_20_norm_out_weight_to_fp16 = const()[name = string("module_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221570624)))]; + tensor module_layers_20_norm_out_bias_to_fp16 = const()[name = string("module_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221572736)))]; + tensor input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = string("input_1111_cast_fp16")]; + tensor input_1113_axes_0 = const()[name = string("input_1113_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221574848)))]; + tensor module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221576960)))]; + tensor input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = string("input_1113_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221579072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223152000))))[name = string("module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1113_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223217600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224790528))))[name = string("module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1117_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_3655_to_fp16 = const()[name = string("op_3655_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3656_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3655_to_fp16)[name = string("op_3656_cast_fp16")]; + tensor input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3656_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224806976)))]; + tensor module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224809088)))]; + tensor query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = string("query_43_cast_fp16")]; + tensor module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224811200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225204480))))[name = string("module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_3672 = const()[name = string("op_3672"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_3672, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225220928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225614208))))[name = string("module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_3676 = const()[name = string("op_3676"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_3676, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225630656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226023936))))[name = string("module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_3680 = const()[name = string("op_3680"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_3680, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226040384)))]; + tensor var_3692_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_3692_cast_fp16")]; + tensor module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226042496)))]; + tensor var_3694_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_3694_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_469_transpose_x_0 = const()[name = string("x_469_transpose_x_0"), val = bool(false)]; + bool x_469_transpose_y_0 = const()[name = string("x_469_transpose_y_0"), val = bool(false)]; + tensor var_3696_to_fp16 = const()[name = string("op_3696_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226044608)))]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3694_cast_fp16)[name = string("transpose_165")]; + tensor x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = var_3696_to_fp16)[name = string("x_469_cast_fp16")]; + tensor x_471_pad_0 = const()[name = string("x_471_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_471_mode_0 = const()[name = string("x_471_mode_0"), val = string("constant")]; + fp16 const_224_to_fp16 = const()[name = string("const_224_to_fp16"), val = fp16(0x0p+0)]; + tensor x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")]; + tensor var_3704 = const()[name = string("op_3704"), val = tensor([1, 8, -1, 188])]; + tensor x_473_cast_fp16 = reshape(shape = var_3704, x = x_471_cast_fp16)[name = string("x_473_cast_fp16")]; + tensor var_3708_begin_0 = const()[name = string("op_3708_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3708_end_0 = const()[name = string("op_3708_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3708_end_mask_0 = const()[name = string("op_3708_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3708_cast_fp16 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = x_473_cast_fp16)[name = string("op_3708_cast_fp16")]; + tensor var_3709 = const()[name = string("op_3709"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_3709, x = var_3708_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3692_cast_fp16)[name = string("transpose_164")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_3718_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_3718_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_3718_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = string("scores_87_cast_fp16")]; + tensor var_3724_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = string("op_3724_cast_fp16")]; + tensor input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3724_cast_fp16, cond = mask_3)[name = string("input_1125_cast_fp16")]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_45_cast_fp16)[name = string("x_475_cast_fp16")]; + tensor var_3728_perm_0 = const()[name = string("op_3728_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3729 = const()[name = string("op_3729"), val = tensor([1, -1, 1024])]; + tensor var_3728_cast_fp16 = transpose(perm = var_3728_perm_0, x = x_475_cast_fp16)[name = string("transpose_161")]; + tensor input_1127_cast_fp16 = reshape(shape = var_3729, x = var_3728_cast_fp16)[name = string("input_1127_cast_fp16")]; + tensor module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226812672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227205952))))[name = string("module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1127_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1131_cast_fp16")]; + tensor x_479_axes_0 = const()[name = string("x_479_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("module_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227222400)))]; + tensor module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("module_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227224512)))]; + tensor x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor input_1133_perm_0 = const()[name = string("input_1133_perm_0"), val = tensor([0, 2, 1])]; + string input_1135_pad_type_0 = const()[name = string("input_1135_pad_type_0"), val = string("valid")]; + tensor input_1135_strides_0 = const()[name = string("input_1135_strides_0"), val = tensor([1])]; + tensor input_1135_pad_0 = const()[name = string("input_1135_pad_0"), val = tensor([0, 0])]; + tensor input_1135_dilations_0 = const()[name = string("input_1135_dilations_0"), val = tensor([1])]; + int32 input_1135_groups_0 = const()[name = string("input_1135_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227226624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228013120))))[name = string("module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = string("transpose_160")]; + tensor input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1133_cast_fp16)[name = string("input_1135_cast_fp16")]; + int32 x_481_split_num_splits_0 = const()[name = string("x_481_split_num_splits_0"), val = int32(2)]; + int32 x_481_split_axis_0 = const()[name = string("x_481_split_axis_0"), val = int32(1)]; + tensor x_481_split_cast_fp16_0, tensor x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = string("x_481_split_cast_fp16")]; + tensor x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = string("x_481_split_1_sigmoid_cast_fp16")]; + tensor x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_328)[name = string("input_1137_cast_fp16")]; + tensor input_1139_pad_0 = const()[name = string("input_1139_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1139_mode_0 = const()[name = string("input_1139_mode_0"), val = string("constant")]; + fp16 const_227_to_fp16 = const()[name = string("const_227_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = string("input_1139_cast_fp16")]; + string input_1141_pad_type_0 = const()[name = string("input_1141_pad_type_0"), val = string("valid")]; + int32 input_1141_groups_0 = const()[name = string("input_1141_groups_0"), val = int32(1024)]; + tensor input_1141_strides_0 = const()[name = string("input_1141_strides_0"), val = tensor([1])]; + tensor input_1141_pad_0 = const()[name = string("input_1141_pad_0"), val = tensor([0, 0])]; + tensor input_1141_dilations_0 = const()[name = string("input_1141_dilations_0"), val = tensor([1])]; + tensor const_290_to_fp16 = const()[name = string("const_290_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228045952)))]; + tensor const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228064448)))]; + tensor input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16, x = input_1139_cast_fp16)[name = string("input_1143_cast_fp16")]; + tensor input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = string("input_1145_cast_fp16")]; + string x_483_pad_type_0 = const()[name = string("x_483_pad_type_0"), val = string("valid")]; + tensor x_483_strides_0 = const()[name = string("x_483_strides_0"), val = tensor([1])]; + tensor x_483_pad_0 = const()[name = string("x_483_pad_0"), val = tensor([0, 0])]; + tensor x_483_dilations_0 = const()[name = string("x_483_dilations_0"), val = tensor([1])]; + int32 x_483_groups_0 = const()[name = string("x_483_groups_0"), val = int32(1)]; + tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228066560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228459840))))[name = string("module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = module_layers_21_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1145_cast_fp16)[name = string("x_483_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = string("transpose_159")]; + tensor input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = string("input_1149_cast_fp16")]; + tensor input_1151_axes_0 = const()[name = string("input_1151_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228476288)))]; + tensor module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228478400)))]; + tensor input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228480512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230053440))))[name = string("module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1151_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230119040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231691968))))[name = string("module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1155_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_3789_to_fp16 = const()[name = string("op_3789_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3790_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3789_to_fp16)[name = string("op_3790_cast_fp16")]; + tensor input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3790_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor input_1163_axes_0 = const()[name = string("input_1163_axes_0"), val = tensor([-1])]; + tensor module_layers_21_norm_out_weight_to_fp16 = const()[name = string("module_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231708416)))]; + tensor module_layers_21_norm_out_bias_to_fp16 = const()[name = string("module_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231710528)))]; + tensor input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = string("input_1163_cast_fp16")]; + tensor input_1165_axes_0 = const()[name = string("input_1165_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231712640)))]; + tensor module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231714752)))]; + tensor input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = string("input_1165_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231716864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233289792))))[name = string("module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1165_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233355392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234928320))))[name = string("module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1169_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_3818_to_fp16 = const()[name = string("op_3818_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3819_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3818_to_fp16)[name = string("op_3819_cast_fp16")]; + tensor input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3819_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234944768)))]; + tensor module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234946880)))]; + tensor query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = string("query_45_cast_fp16")]; + tensor module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234948992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235342272))))[name = string("module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_3835 = const()[name = string("op_3835"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_3835, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235358720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235752000))))[name = string("module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_3839 = const()[name = string("op_3839"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_3839, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235768448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236161728))))[name = string("module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_3843 = const()[name = string("op_3843"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_3843, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236178176)))]; + tensor var_3855_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_3855_cast_fp16")]; + tensor module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236180288)))]; + tensor var_3857_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_3857_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_491_transpose_x_0 = const()[name = string("x_491_transpose_x_0"), val = bool(false)]; + bool x_491_transpose_y_0 = const()[name = string("x_491_transpose_y_0"), val = bool(false)]; + tensor var_3859_to_fp16 = const()[name = string("op_3859_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236182400)))]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3857_cast_fp16)[name = string("transpose_158")]; + tensor x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = var_3859_to_fp16)[name = string("x_491_cast_fp16")]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_493_mode_0 = const()[name = string("x_493_mode_0"), val = string("constant")]; + fp16 const_234_to_fp16 = const()[name = string("const_234_to_fp16"), val = fp16(0x0p+0)]; + tensor x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor var_3867 = const()[name = string("op_3867"), val = tensor([1, 8, -1, 188])]; + tensor x_495_cast_fp16 = reshape(shape = var_3867, x = x_493_cast_fp16)[name = string("x_495_cast_fp16")]; + tensor var_3871_begin_0 = const()[name = string("op_3871_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3871_end_0 = const()[name = string("op_3871_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_3871_end_mask_0 = const()[name = string("op_3871_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3871_cast_fp16 = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = x_495_cast_fp16)[name = string("op_3871_cast_fp16")]; + tensor var_3872 = const()[name = string("op_3872"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_3872, x = var_3871_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3855_cast_fp16)[name = string("transpose_157")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_3881_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_3881_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_3881_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = string("scores_91_cast_fp16")]; + tensor var_3887_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = string("op_3887_cast_fp16")]; + tensor input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_3887_cast_fp16, cond = mask_3)[name = string("input_1177_cast_fp16")]; + bool x_497_transpose_x_0 = const()[name = string("x_497_transpose_x_0"), val = bool(false)]; + bool x_497_transpose_y_0 = const()[name = string("x_497_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")]; + tensor x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_47_cast_fp16)[name = string("x_497_cast_fp16")]; + tensor var_3891_perm_0 = const()[name = string("op_3891_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3892 = const()[name = string("op_3892"), val = tensor([1, -1, 1024])]; + tensor var_3891_cast_fp16 = transpose(perm = var_3891_perm_0, x = x_497_cast_fp16)[name = string("transpose_154")]; + tensor input_1179_cast_fp16 = reshape(shape = var_3892, x = var_3891_cast_fp16)[name = string("input_1179_cast_fp16")]; + tensor module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236950464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237343744))))[name = string("module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1179_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1183_cast_fp16")]; + tensor x_501_axes_0 = const()[name = string("x_501_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("module_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237360192)))]; + tensor module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("module_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237362304)))]; + tensor x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = string("x_501_cast_fp16")]; + tensor input_1185_perm_0 = const()[name = string("input_1185_perm_0"), val = tensor([0, 2, 1])]; + string input_1187_pad_type_0 = const()[name = string("input_1187_pad_type_0"), val = string("valid")]; + tensor input_1187_strides_0 = const()[name = string("input_1187_strides_0"), val = tensor([1])]; + tensor input_1187_pad_0 = const()[name = string("input_1187_pad_0"), val = tensor([0, 0])]; + tensor input_1187_dilations_0 = const()[name = string("input_1187_dilations_0"), val = tensor([1])]; + int32 input_1187_groups_0 = const()[name = string("input_1187_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237364416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238150912))))[name = string("module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = string("transpose_153")]; + tensor input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1185_cast_fp16)[name = string("input_1187_cast_fp16")]; + int32 x_503_split_num_splits_0 = const()[name = string("x_503_split_num_splits_0"), val = int32(2)]; + int32 x_503_split_axis_0 = const()[name = string("x_503_split_axis_0"), val = int32(1)]; + tensor x_503_split_cast_fp16_0, tensor x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = string("x_503_split_cast_fp16")]; + tensor x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = string("x_503_split_1_sigmoid_cast_fp16")]; + tensor x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_328)[name = string("input_1189_cast_fp16")]; + tensor input_1191_pad_0 = const()[name = string("input_1191_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1191_mode_0 = const()[name = string("input_1191_mode_0"), val = string("constant")]; + fp16 const_237_to_fp16 = const()[name = string("const_237_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = string("input_1191_cast_fp16")]; + string input_1193_pad_type_0 = const()[name = string("input_1193_pad_type_0"), val = string("valid")]; + int32 input_1193_groups_0 = const()[name = string("input_1193_groups_0"), val = int32(1024)]; + tensor input_1193_strides_0 = const()[name = string("input_1193_strides_0"), val = tensor([1])]; + tensor input_1193_pad_0 = const()[name = string("input_1193_pad_0"), val = tensor([0, 0])]; + tensor input_1193_dilations_0 = const()[name = string("input_1193_dilations_0"), val = tensor([1])]; + tensor const_292_to_fp16 = const()[name = string("const_292_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238183744)))]; + tensor const_293_to_fp16 = const()[name = string("const_293_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238202240)))]; + tensor input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16, x = input_1191_cast_fp16)[name = string("input_1195_cast_fp16")]; + tensor input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = string("input_1197_cast_fp16")]; + string x_505_pad_type_0 = const()[name = string("x_505_pad_type_0"), val = string("valid")]; + tensor x_505_strides_0 = const()[name = string("x_505_strides_0"), val = tensor([1])]; + tensor x_505_pad_0 = const()[name = string("x_505_pad_0"), val = tensor([0, 0])]; + tensor x_505_dilations_0 = const()[name = string("x_505_dilations_0"), val = tensor([1])]; + int32 x_505_groups_0 = const()[name = string("x_505_groups_0"), val = int32(1)]; + tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238204352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238597632))))[name = string("module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = module_layers_22_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1197_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = string("transpose_152")]; + tensor input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = string("input_1201_cast_fp16")]; + tensor input_1203_axes_0 = const()[name = string("input_1203_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238614080)))]; + tensor module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238616192)))]; + tensor input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238618304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240191232))))[name = string("module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1203_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240256832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241829760))))[name = string("module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1207_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_3952_to_fp16 = const()[name = string("op_3952_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3953_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3952_to_fp16)[name = string("op_3953_cast_fp16")]; + tensor input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3953_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor input_1215_axes_0 = const()[name = string("input_1215_axes_0"), val = tensor([-1])]; + tensor module_layers_22_norm_out_weight_to_fp16 = const()[name = string("module_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241846208)))]; + tensor module_layers_22_norm_out_bias_to_fp16 = const()[name = string("module_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241848320)))]; + tensor input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = string("input_1215_cast_fp16")]; + tensor input_1217_axes_0 = const()[name = string("input_1217_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241850432)))]; + tensor module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241852544)))]; + tensor input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = string("input_1217_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241854656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243427584))))[name = string("module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue")]; + tensor linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_fivevalue, x = input_1217_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243493184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245066112))))[name = string("module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue")]; + tensor linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_fivevalue, x = input_1221_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_3981_to_fp16 = const()[name = string("op_3981_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3982_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3981_to_fp16)[name = string("op_3982_cast_fp16")]; + tensor input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3982_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor query_axes_0 = const()[name = string("query_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245082560)))]; + tensor module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245084672)))]; + tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = string("query_cast_fp16")]; + tensor module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245086784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245480064))))[name = string("module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue")]; + tensor linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_3998 = const()[name = string("op_3998"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_3998, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245496512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245889792))))[name = string("module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue")]; + tensor linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_4002 = const()[name = string("op_4002"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_4002, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245906240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246299520))))[name = string("module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue")]; + tensor linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_fivevalue, x = query_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_4006 = const()[name = string("op_4006"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_4006, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246315968)))]; + tensor var_4018_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4018_cast_fp16")]; + tensor module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246318080)))]; + tensor var_4020_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4020_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_513_transpose_x_0 = const()[name = string("x_513_transpose_x_0"), val = bool(false)]; + bool x_513_transpose_y_0 = const()[name = string("x_513_transpose_y_0"), val = bool(false)]; + tensor var_4022_to_fp16 = const()[name = string("op_4022_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246320192)))]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4020_cast_fp16)[name = string("transpose_151")]; + tensor x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_4022_to_fp16)[name = string("x_513_cast_fp16")]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_515_mode_0 = const()[name = string("x_515_mode_0"), val = string("constant")]; + fp16 const_244_to_fp16 = const()[name = string("const_244_to_fp16"), val = fp16(0x0p+0)]; + tensor x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor var_4030 = const()[name = string("op_4030"), val = tensor([1, 8, -1, 188])]; + tensor x_517_cast_fp16 = reshape(shape = var_4030, x = x_515_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor var_4034_begin_0 = const()[name = string("op_4034_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4034_end_0 = const()[name = string("op_4034_end_0"), val = tensor([1, 8, 376, 188])]; + tensor var_4034_end_mask_0 = const()[name = string("op_4034_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4034_cast_fp16 = slice_by_index(begin = var_4034_begin_0, end = var_4034_end_0, end_mask = var_4034_end_mask_0, x = x_517_cast_fp16)[name = string("op_4034_cast_fp16")]; + tensor var_4035 = const()[name = string("op_4035"), val = tensor([1, 8, 188, 375])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4035, x = var_4034_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4018_cast_fp16)[name = string("transpose_150")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_4044_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4044_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_4044_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = string("scores_cast_fp16")]; + tensor var_4050_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_4050_cast_fp16")]; + tensor input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4050_cast_fp16, cond = mask_3)[name = string("input_1229_cast_fp16")]; + bool x_519_transpose_x_0 = const()[name = string("x_519_transpose_x_0"), val = bool(false)]; + bool x_519_transpose_y_0 = const()[name = string("x_519_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")]; + tensor x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor var_4054_perm_0 = const()[name = string("op_4054_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4055 = const()[name = string("op_4055"), val = tensor([1, -1, 1024])]; + tensor var_4054_cast_fp16 = transpose(perm = var_4054_perm_0, x = x_519_cast_fp16)[name = string("transpose_147")]; + tensor input_1231_cast_fp16 = reshape(shape = var_4055, x = var_4054_cast_fp16)[name = string("input_1231_cast_fp16")]; + tensor module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247088256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247481536))))[name = string("module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue")]; + tensor linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_fivevalue, x = input_1231_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1235_cast_fp16")]; + tensor x_523_axes_0 = const()[name = string("x_523_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("module_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247497984)))]; + tensor module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("module_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247500096)))]; + tensor x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = string("x_523_cast_fp16")]; + tensor input_1237_perm_0 = const()[name = string("input_1237_perm_0"), val = tensor([0, 2, 1])]; + string input_1239_pad_type_0 = const()[name = string("input_1239_pad_type_0"), val = string("valid")]; + tensor input_1239_strides_0 = const()[name = string("input_1239_strides_0"), val = tensor([1])]; + tensor input_1239_pad_0 = const()[name = string("input_1239_pad_0"), val = tensor([0, 0])]; + tensor input_1239_dilations_0 = const()[name = string("input_1239_dilations_0"), val = tensor([1])]; + int32 input_1239_groups_0 = const()[name = string("input_1239_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247502208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248288704))))[name = string("module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue")]; + tensor input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = string("transpose_146")]; + tensor input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_fivevalue, x = input_1237_cast_fp16)[name = string("input_1239_cast_fp16")]; + int32 x_525_split_num_splits_0 = const()[name = string("x_525_split_num_splits_0"), val = int32(2)]; + int32 x_525_split_axis_0 = const()[name = string("x_525_split_axis_0"), val = int32(1)]; + tensor x_525_split_cast_fp16_0, tensor x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = string("x_525_split_cast_fp16")]; + tensor x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = string("x_525_split_1_sigmoid_cast_fp16")]; + tensor x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = string("x_525_cast_fp16")]; + tensor input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_328)[name = string("input_1241_cast_fp16")]; + tensor input_1243_pad_0 = const()[name = string("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_1243_mode_0 = const()[name = string("input_1243_mode_0"), val = string("constant")]; + fp16 const_247_to_fp16 = const()[name = string("const_247_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = string("input_1243_cast_fp16")]; + string input_1245_pad_type_0 = const()[name = string("input_1245_pad_type_0"), val = string("valid")]; + int32 input_1245_groups_0 = const()[name = string("input_1245_groups_0"), val = int32(1024)]; + tensor input_1245_strides_0 = const()[name = string("input_1245_strides_0"), val = tensor([1])]; + tensor input_1245_pad_0 = const()[name = string("input_1245_pad_0"), val = tensor([0, 0])]; + tensor input_1245_dilations_0 = const()[name = string("input_1245_dilations_0"), val = tensor([1])]; + tensor const_294_to_fp16 = const()[name = string("const_294_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248321536)))]; + tensor const_295_to_fp16 = const()[name = string("const_295_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248340032)))]; + tensor input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16, x = input_1243_cast_fp16)[name = string("input_1247_cast_fp16")]; + tensor input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = string("input_1249_cast_fp16")]; + string x_527_pad_type_0 = const()[name = string("x_527_pad_type_0"), val = string("valid")]; + tensor x_527_strides_0 = const()[name = string("x_527_strides_0"), val = tensor([1])]; + tensor x_527_pad_0 = const()[name = string("x_527_pad_0"), val = tensor([0, 0])]; + tensor x_527_dilations_0 = const()[name = string("x_527_dilations_0"), val = tensor([1])]; + int32 x_527_groups_0 = const()[name = string("x_527_groups_0"), val = int32(1)]; + tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248342144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248735424))))[name = string("module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue")]; + tensor x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = module_layers_23_conv_pointwise_conv2_weight_to_fp16_fivevalue, x = input_1249_cast_fp16)[name = string("x_527_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = string("transpose_145")]; + tensor input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = string("input_1253_cast_fp16")]; + tensor input_1255_axes_0 = const()[name = string("input_1255_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248751872)))]; + tensor module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248753984)))]; + tensor input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248756096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250329024))))[name = string("module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue")]; + tensor linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_fivevalue, x = input_1255_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250394624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251967552))))[name = string("module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue")]; + tensor linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_fivevalue, x = input_1259_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_4115_to_fp16 = const()[name = string("op_4115_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4116_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4115_to_fp16)[name = string("op_4116_cast_fp16")]; + tensor input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4116_cast_fp16)[name = string("input_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor module_layers_23_norm_out_weight_to_fp16 = const()[name = string("module_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251984000)))]; + tensor module_layers_23_norm_out_bias_to_fp16 = const()[name = string("module_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251986112)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")]; + tensor obj_1_perm_0 = const()[name = string("obj_1_perm_0"), val = tensor([0, 2, 1])]; + string obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")]; + tensor encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = string("cast_0")]; + } -> (encoder, encoder_length); +} \ No newline at end of file diff --git a/bark-3.0/Encoder_lut3.mlmodelc/weights/weight.bin b/bark-3.0/Encoder_lut3.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..554490f7622e371984bcd2c7aa5af66e4ed01cf0 --- /dev/null +++ b/bark-3.0/Encoder_lut3.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a1e76627c0eef7fb1b4da209520f753352c8ae4edb71086abdba5bc416e862a +size 251988224 diff --git a/bark-3.0/JointDecisionv3.mlmodelc/analytics/coremldata.bin b/bark-3.0/JointDecisionv3.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..861e04524dc948177e7106c26ac84fb101b0d044 --- /dev/null +++ b/bark-3.0/JointDecisionv3.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b527affb76296ea13a810f12cb73c4e6303dca68f3830dd28f8c3d60fe22987 +size 243 diff --git a/bark-3.0/JointDecisionv3.mlmodelc/coremldata.bin b/bark-3.0/JointDecisionv3.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a175373af0f499da388b4a8f857190ed6db6ec3a --- /dev/null +++ b/bark-3.0/JointDecisionv3.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e80f5a625d6df5b41d174bf2837b442d51382532bfd32995dbb1919afd9bfa1 +size 595 diff --git a/bark-3.0/JointDecisionv3.mlmodelc/metadata.json b/bark-3.0/JointDecisionv3.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9d66397bc6760e9ff12f90ebb542bba09a3dfa78 --- /dev/null +++ b/bark-3.0/JointDecisionv3.mlmodelc/metadata.json @@ -0,0 +1,124 @@ +[ + { + "metadataOutputVersion" : "3.0", + "shortDescription" : "Underdog Bark 3.0 single-step joint decision (top-K 64)", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1, 1]", + "name" : "token_id", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1, 1]", + "name" : "token_prob", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1 × 1)", + "shortDescription" : "", + "shape" : "[1, 1, 1]", + "name" : "duration", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 1 × 1 × 64)", + "shortDescription" : "", + "shape" : "[1, 1, 1, 64]", + "name" : "top_k_ids", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1 × 1 × 64)", + "shortDescription" : "", + "shape" : "[1, 1, 1, 64]", + "name" : "top_k_logits", + "type" : "MultiArray" + } + ], + "storagePrecision" : "Float16", + "modelParameters" : [ + + ], + "author" : "Fluid Inference", + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Ios17.reduceArgmax" : 2, + "Ios17.linear" : 3, + "Ios17.transpose" : 2, + "Ios17.sliceByIndex" : 2, + "Ios17.add" : 1, + "Ios17.topk" : 1, + "Ios16.relu" : 1, + "Ios16.softmax" : 1, + "Ios17.expandDims" : 3, + "Ios17.squeeze" : 1, + "Ios17.cast" : 6, + "Ios17.gatherAlongAxis" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1024 × 1)", + "shortDescription" : "", + "shape" : "[1, 1024, 1]", + "name" : "encoder_step", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 640 × 1)", + "shortDescription" : "", + "shape" : "[1, 640, 1]", + "name" : "decoder_step", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-10-21", + "com.github.apple.coremltools.source" : "torch==2.7.0", + "com.github.apple.coremltools.version" : "9.0b1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "JointDecisionv3", + "method" : "predict" + } +] \ No newline at end of file diff --git a/bark-3.0/JointDecisionv3.mlmodelc/model.mil b/bark-3.0/JointDecisionv3.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..322a3748e073a27aefd42984cfa25e26bb52b63f --- /dev/null +++ b/bark-3.0/JointDecisionv3.mlmodelc/model.mil @@ -0,0 +1,69 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] +{ + func main(tensor decoder_step, tensor encoder_step) { + tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; + tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; + tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1310848)))]; + tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_5")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312192)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2131456)))]; + tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_4")]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; + tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; + tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; + tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; + tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2132800)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12626304)))]; + tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; + tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 8193])]; + tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; + tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; + tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 8193])]; + tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 8198])]; + tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; + tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; + tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; + tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; + tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; + tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; + tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; + tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; + tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; + tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; + tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; + tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; + tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_3")]; + tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; + tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; + tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; + tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; + tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; + tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; + tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; + tensor var_72 = const()[name = tensor("op_72"), val = tensor(64)]; + tensor var_76_axis_0 = const()[name = tensor("op_76_axis_0"), val = tensor(-1)]; + tensor var_76_ascending_0 = const()[name = tensor("op_76_ascending_0"), val = tensor(false)]; + tensor var_76_sort_0 = const()[name = tensor("op_76_sort_0"), val = tensor(true)]; + tensor var_76_return_indices_0 = const()[name = tensor("op_76_return_indices_0"), val = tensor(true)]; + tensor var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor("uint16")]; + tensor var_76_cast_fp16_cast_int16_0, tensor var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor("op_76_cast_fp16_cast_int16")]; + tensor var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor("int32")]; + tensor var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor("cast_0")]; + tensor top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor("cast_1")]; + tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_2")]; + } -> (token_id, token_prob, duration, top_k_ids, top_k_logits); +} \ No newline at end of file diff --git a/bark-3.0/JointDecisionv3.mlmodelc/weights/weight.bin b/bark-3.0/JointDecisionv3.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..253a9149d87b88d776f86480ae9a49965dd23535 --- /dev/null +++ b/bark-3.0/JointDecisionv3.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aab5aacdd31dc5644ab3f000f38bc8d94e30d50da585ba49165fa5872a53152a +size 12642764 diff --git a/bark-3.0/NOTICE b/bark-3.0/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..a2baabfd6a84b4fe3a60ceb517f9e9c47b270380 --- /dev/null +++ b/bark-3.0/NOTICE @@ -0,0 +1,13 @@ +Underdog Bark 3.0 (ConwayResearch/Underdog-Bark-3.0), part of the Bark 3.0 family + +This model derives from: + + nvidia/parakeet-tdt-0.6b-v3, (c) NVIDIA Corporation, CC-BY-4.0 + https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 + moondream/parakeet-ultra, (c) Moondream, CC-BY-4.0 + https://huggingface.co/moondream/parakeet-ultra + +License: https://creativecommons.org/licenses/by/4.0/ + +Changes: post-trained and compressed by Conway Research. The FastConformer encoder architecture, TDT decoder, +tokenizer and feature-extractor configuration derive from the models above and remain under CC-BY-4.0. diff --git a/bark-3.0/Preprocessor.mlmodelc/analytics/coremldata.bin b/bark-3.0/Preprocessor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cadf776b6eddc28eae76654cdff21b7c61efbd78 --- /dev/null +++ b/bark-3.0/Preprocessor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9beeb989c8d66f8be11df59bc6df277ec76cee404f6865b46243835ef562f6d +size 243 diff --git a/bark-3.0/Preprocessor.mlmodelc/coremldata.bin b/bark-3.0/Preprocessor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c86b363f1bbf6a4f38ec5757390b7ac17e99adda --- /dev/null +++ b/bark-3.0/Preprocessor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dbde3f2300842c1fd51ef3ff948a0bcffe65ffd2dca10707f2509f32c1d65b1d +size 486 diff --git a/bark-3.0/Preprocessor.mlmodelc/metadata.json b/bark-3.0/Preprocessor.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..06162c58cf2f5343a0e60f36f843bcdf3cc58527 --- /dev/null +++ b/bark-3.0/Preprocessor.mlmodelc/metadata.json @@ -0,0 +1,105 @@ +[ + { + "metadataOutputVersion" : "3.0", + "shortDescription" : "int8-linear quantized - preprocessor", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 128 × 1501)", + "shortDescription" : "", + "shape" : "[1, 128, 1501]", + "name" : "mel", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "mel_length", + "type" : "MultiArray" + } + ], + "storagePrecision" : "Int8", + "modelParameters" : [ + + ], + "author" : "Fluid Inference", + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Ios17.reshape" : 2, + "Ios17.matmul" : 1, + "Select" : 3, + "Ios17.expandDims" : 8, + "Ios17.add" : 4, + "Tile" : 1, + "Ios17.sliceByIndex" : 3, + "Ios16.reduceSum" : 4, + "Pad" : 1, + "Ios17.log" : 1, + "Ios16.constexprAffineDequantize" : 4, + "Ios17.less" : 1, + "Ios17.sub" : 4, + "Ios17.conv" : 2, + "Ios17.pow" : 2, + "Ios17.cast" : 5, + "Stack" : 1, + "Ios17.concat" : 1, + "Ios17.floorDiv" : 1, + "Ios17.realDiv" : 3, + "Ios17.sqrt" : 1, + "Ios17.greaterEqual" : 1, + "Ios17.mul" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 240000)", + "shortDescription" : "", + "shape" : "[1, 240000]", + "name" : "audio_signal", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "audio_length", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2025-09-19", + "com.github.apple.coremltools.source" : "torch==2.7.0", + "com.github.apple.coremltools.version" : "9.0b1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "parakeet_preprocessor", + "method" : "predict" + } +] \ No newline at end of file diff --git a/bark-3.0/Preprocessor.mlmodelc/model.mil b/bark-3.0/Preprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..f987e2479228436c49189aa47edb376db74dcff5 --- /dev/null +++ b/bark-3.0/Preprocessor.mlmodelc/model.mil @@ -0,0 +1,125 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] +{ + func main(tensor audio_length, tensor audio_signal) { + tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; + tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; + tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; + tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; + tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; + tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; + tensor var_38_to_fp16_dtype_0 = const()[name = tensor("op_38_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor var_39_promoted_to_fp16 = const()[name = tensor("op_39_promoted_to_fp16"), val = tensor(0x1p+0)]; + tensor floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor("cast_4")]; + tensor seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor("seq_len_1_cast_fp16")]; + tensor seq_len_dtype_0 = const()[name = tensor("seq_len_dtype_0"), val = tensor("int32")]; + tensor var_43_begin_0 = const()[name = tensor("op_43_begin_0"), val = tensor([0, 0])]; + tensor var_43_end_0 = const()[name = tensor("op_43_end_0"), val = tensor([1, 1])]; + tensor var_43_end_mask_0 = const()[name = tensor("op_43_end_mask_0"), val = tensor([true, false])]; + tensor var_43_squeeze_mask_0 = const()[name = tensor("op_43_squeeze_mask_0"), val = tensor([false, true])]; + tensor audio_signal_to_fp16_dtype_0 = const()[name = tensor("audio_signal_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor("cast_3")]; + tensor var_43_cast_fp16 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor("op_43_cast_fp16")]; + tensor var_44_axes_0 = const()[name = tensor("op_44_axes_0"), val = tensor([1])]; + tensor var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor("op_44_cast_fp16")]; + tensor var_46_begin_0 = const()[name = tensor("op_46_begin_0"), val = tensor([0, 1])]; + tensor var_46_end_0 = const()[name = tensor("op_46_end_0"), val = tensor([1, 240000])]; + tensor var_46_end_mask_0 = const()[name = tensor("op_46_end_mask_0"), val = tensor([true, true])]; + tensor var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_46_cast_fp16")]; + tensor var_48_begin_0 = const()[name = tensor("op_48_begin_0"), val = tensor([0, 0])]; + tensor var_48_end_0 = const()[name = tensor("op_48_end_0"), val = tensor([1, 239999])]; + tensor var_48_end_mask_0 = const()[name = tensor("op_48_end_mask_0"), val = tensor([true, false])]; + tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_48_cast_fp16")]; + tensor var_49_to_fp16 = const()[name = tensor("op_49_to_fp16"), val = tensor(0x1.f0cp-1)]; + tensor var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor("op_50_cast_fp16")]; + tensor var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor("op_51_cast_fp16")]; + tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; + tensor input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_cast_fp16))[name = tensor("input_1_cast_fp16")]; + tensor var_57 = const()[name = tensor("op_57"), val = tensor([1, 1, 240000])]; + tensor input_3_cast_fp16 = reshape(shape = var_57, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; + tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("reflect")]; + tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_63 = const()[name = tensor("op_63"), val = tensor([1, 240512])]; + tensor input_cast_fp16 = reshape(shape = var_63, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor expand_dims_5 = const()[name = tensor("expand_dims_5"), val = tensor([160])]; + tensor expand_dims_6_axes_0 = const()[name = tensor("expand_dims_6_axes_0"), val = tensor([1])]; + tensor expand_dims_6_cast_fp16 = expand_dims(axes = expand_dims_6_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_6_cast_fp16")]; + tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; + tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; + tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; + tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; + tensor expand_dims_3_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_3_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132096))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131712)))]; + tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_5, weight = expand_dims_3_to_fp16_quantized, x = expand_dims_6_cast_fp16)[name = tensor("conv_0_cast_fp16")]; + tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; + tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; + tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; + tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; + tensor expand_dims_4_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_4_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132736))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264768))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264384)))]; + tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_5, weight = expand_dims_4_to_fp16_quantized, x = expand_dims_6_cast_fp16)[name = tensor("conv_1_cast_fp16")]; + tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; + tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; + tensor var_17_promoted_to_fp16 = const()[name = tensor("op_17_promoted_to_fp16"), val = tensor(0x1p+1)]; + tensor var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor("op_67_cast_fp16")]; + tensor var_69_axes_0 = const()[name = tensor("op_69_axes_0"), val = tensor([-1])]; + tensor var_69_keep_dims_0 = const()[name = tensor("op_69_keep_dims_0"), val = tensor(false)]; + tensor var_69_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor("op_69_cast_fp16")]; + tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; + tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; + tensor const_6_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(1), name = tensor("const_6_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265408))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298560))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298368)))]; + tensor x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_6_to_fp16_quantized, y = var_69_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor var_76_to_fp16 = const()[name = tensor("op_76_to_fp16"), val = tensor(0x1p-24)]; + tensor var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor("op_77_cast_fp16")]; + tensor x_13_epsilon_0 = const()[name = tensor("x_13_epsilon_0"), val = tensor(0x1p-149)]; + tensor x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor var_82 = const()[name = tensor("op_82"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])]; + tensor var_85_axes_0 = const()[name = tensor("op_85_axes_0"), val = tensor([1])]; + tensor mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor("cast_2")]; + tensor var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor("op_85")]; + tensor valid_mask = less(x = var_82, y = var_85)[name = tensor("valid_mask")]; + tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; + tensor var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor("op_87")]; + tensor var_87_after_broadcast_reps_0 = const()[name = tensor("op_87_after_broadcast_reps_0"), val = tensor([1, 128, 1])]; + tensor var_87_after_broadcast = tile(reps = var_87_after_broadcast_reps_0, x = var_87)[name = tensor("op_87_after_broadcast")]; + tensor op_24_after_broadcast_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("op_24_after_broadcast_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298880))), scale = tensor(0x0p+0), zero_point = tensor(0)]; + tensor var_88_cast_fp16 = select(a = x_13_cast_fp16, b = op_24_after_broadcast_to_fp16_quantized, cond = var_87_after_broadcast)[name = tensor("op_88_cast_fp16")]; + tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; + tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; + tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; + tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; + tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; + tensor cast_2_to_fp16_dtype_0 = const()[name = tensor("cast_2_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_1")]; + tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; + tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; + tensor var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_93_cast_fp16")]; + tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor("x_mean_cast_fp16")]; + tensor var_96_axes_0 = const()[name = tensor("op_96_axes_0"), val = tensor([2])]; + tensor var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor("op_96_cast_fp16")]; + tensor var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = select(a = var_97_cast_fp16, b = op_24_after_broadcast_to_fp16_quantized, cond = var_87_after_broadcast)[name = tensor("op_98_cast_fp16")]; + tensor var_17_promoted_1_to_fp16 = const()[name = tensor("op_17_promoted_1_to_fp16"), val = tensor(0x1p+1)]; + tensor var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor("op_99_cast_fp16")]; + tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([2])]; + tensor var_101_keep_dims_0 = const()[name = tensor("op_101_keep_dims_0"), val = tensor(false)]; + tensor var_101_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor("op_101_cast_fp16")]; + tensor var_103_to_fp16 = const()[name = tensor("op_103_to_fp16"), val = tensor(0x1p+0)]; + tensor var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor("op_104_cast_fp16")]; + tensor var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; + tensor x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; + tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; + tensor x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; + tensor var_110_axes_0 = const()[name = tensor("op_110_axes_0"), val = tensor([2])]; + tensor var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor("op_110_cast_fp16")]; + tensor x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor mask = greater_equal(x = var_82, y = var_85)[name = tensor("mask")]; + tensor var_119_axes_0 = const()[name = tensor("op_119_axes_0"), val = tensor([1])]; + tensor var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor("op_119")]; + tensor var_24_to_fp16 = const()[name = tensor("op_24_to_fp16"), val = tensor(0x0p+0)]; + tensor processed_signal_cast_fp16 = select(a = var_24_to_fp16, b = x_cast_fp16, cond = var_119)[name = tensor("processed_signal_cast_fp16")]; + tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_0")]; + } -> (mel, mel_length); +} \ No newline at end of file diff --git a/bark-3.0/Preprocessor.mlmodelc/weights/weight.bin b/bark-3.0/Preprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..47be051f50981bc166d0bcf6111cff47dd0db117 --- /dev/null +++ b/bark-3.0/Preprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:129b76e3aeafa8afa3ea76d995b964b145fe83700d579f6ff42c4c38fa0968ea +size 491072 diff --git a/bark-3.0/parakeet_vocab.json b/bark-3.0/parakeet_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..c684822ec3b671ebc449c433a7012398d39b8bb0 --- /dev/null +++ b/bark-3.0/parakeet_vocab.json @@ -0,0 +1,8194 @@ +{ + "0": "", + "1": "<|nospeech|>", + "2": "", + "3": "<|endoftext|>", + "4": "<|startoftranscript|>", + "5": "<|pnc|>", + "6": "<|nopnc|>", + "7": "<|startofcontext|>", + "8": "<|itn|>", + "9": "<|noitn|>", + "10": "<|timestamp|>", + "11": "<|notimestamp|>", + "12": "<|diarize|>", + "13": "<|nodiarize|>", + "14": "<|spkchange|>", + "15": "<|audioseparator|>", + "16": "<|emo:undefined|>", + "17": "<|emo:neutral|>", + "18": "<|emo:happy|>", + "19": "<|emo:sad|>", + "20": "<|emo:angry|>", + "21": "<|unklang|>", + "22": "<|predict_lang|>", + "23": "<|nopredict_lang|>", + "24": "<|aa|>", + "25": "<|ab|>", + "26": "<|af|>", + "27": "<|ak|>", + "28": "<|sq|>", + "29": "<|am|>", + "30": "<|ar|>", + "31": "<|an|>", + "32": "<|hy|>", + "33": "<|as|>", + "34": "<|av|>", + "35": "<|ae|>", + "36": "<|ay|>", + "37": "<|az|>", + "38": "<|bm|>", + "39": "<|ba|>", + "40": "<|eu|>", + "41": "<|be|>", + "42": "<|bn|>", + "43": "<|bi|>", + "44": "<|bs|>", + "45": "<|br|>", + "46": "<|bg|>", + "47": "<|my|>", + "48": "<|ca|>", + "49": "<|ch|>", + "50": "<|ce|>", + "51": "<|ny|>", + "52": "<|zh|>", + "53": "<|cu|>", + "54": "<|cv|>", + "55": "<|kw|>", + "56": "<|co|>", + "57": "<|cr|>", + "58": "<|hr|>", + "59": "<|cs|>", + "60": "<|da|>", + "61": "<|dv|>", + "62": "<|nl|>", + "63": "<|dz|>", + "64": "<|en|>", + "65": "<|eo|>", + "66": "<|et|>", + "67": "<|ee|>", + "68": "<|fo|>", + "69": "<|fj|>", + "70": "<|fi|>", + "71": "<|fr|>", + "72": "<|fy|>", + "73": "<|ff|>", + "74": "<|gd|>", + "75": "<|gl|>", + "76": "<|lg|>", + "77": "<|ka|>", + "78": "<|de|>", + "79": "<|el|>", + "80": "<|kl|>", + "81": "<|gn|>", + "82": "<|gu|>", + "83": "<|ht|>", + "84": "<|ha|>", + "85": "<|he|>", + "86": "<|hz|>", + "87": "<|hi|>", + "88": "<|ho|>", + "89": "<|hu|>", + "90": "<|is|>", + "91": "<|io|>", + "92": "<|ig|>", + "93": "<|id|>", + "94": "<|ia|>", + "95": "<|ie|>", + "96": "<|iu|>", + "97": "<|ik|>", + "98": "<|ga|>", + "99": "<|it|>", + "100": "<|ja|>", + "101": "<|jv|>", + "102": "<|kn|>", + "103": "<|kr|>", + "104": "<|ks|>", + "105": "<|kk|>", + "106": "<|km|>", + "107": "<|ki|>", + "108": "<|rw|>", + "109": "<|ky|>", + "110": "<|kv|>", + "111": "<|kg|>", + "112": "<|ko|>", + "113": "<|kj|>", + "114": "<|ku|>", + "115": "<|lo|>", + "116": "<|la|>", + "117": "<|lv|>", + "118": "<|li|>", + "119": "<|ln|>", + "120": "<|lt|>", + "121": "<|lu|>", + "122": "<|lb|>", + "123": "<|mk|>", + "124": "<|mg|>", + "125": "<|ms|>", + "126": "<|ml|>", + "127": "<|mt|>", + "128": "<|gv|>", + "129": "<|mi|>", + "130": "<|mr|>", + "131": "<|mh|>", + "132": "<|mn|>", + "133": "<|na|>", + "134": "<|nv|>", + "135": "<|nd|>", + "136": "<|nr|>", + "137": "<|ng|>", + "138": "<|ne|>", + "139": "<|no|>", + "140": "<|nb|>", + "141": "<|nn|>", + "142": "<|oc|>", + "143": "<|oj|>", + "144": "<|or|>", + "145": "<|om|>", + "146": "<|os|>", + "147": "<|pi|>", + "148": "<|ps|>", + "149": "<|fa|>", + "150": "<|pl|>", + "151": "<|pt|>", + "152": "<|pa|>", + "153": "<|qu|>", + "154": "<|ro|>", + "155": "<|rm|>", + "156": "<|rn|>", + "157": "<|ru|>", + "158": "<|se|>", + "159": "<|sm|>", + "160": "<|sg|>", + "161": "<|sa|>", + "162": "<|sc|>", + "163": "<|sr|>", + "164": "<|sn|>", + "165": "<|sd|>", + "166": "<|si|>", + "167": "<|sk|>", + "168": "<|sl|>", + "169": "<|so|>", + "170": "<|st|>", + "171": "<|es|>", + "172": "<|su|>", + "173": "<|sw|>", + "174": "<|ss|>", + "175": "<|sv|>", + "176": "<|tl|>", + "177": "<|ty|>", + "178": "<|tg|>", + "179": "<|ta|>", + "180": "<|tt|>", + "181": "<|te|>", + "182": "<|th|>", + "183": "<|bo|>", + "184": "<|ti|>", + "185": "<|to|>", + "186": "<|ts|>", + "187": "<|tn|>", + "188": "<|tr|>", + "189": "<|tk|>", + "190": "<|tw|>", + "191": "<|ug|>", + "192": "<|uk|>", + "193": "<|ur|>", + "194": "<|uz|>", + "195": "<|ve|>", + "196": "<|vi|>", + "197": "<|vo|>", + "198": "<|wa|>", + "199": "<|cy|>", + "200": "<|wo|>", + "201": "<|xh|>", + "202": "<|ii|>", + "203": "<|yi|>", + "204": "<|yo|>", + "205": "<|za|>", + "206": "<|zu|>", + "207": "<|0|>", + "208": "<|1|>", + "209": "<|2|>", + "210": "<|3|>", + "211": "<|4|>", + "212": "<|5|>", + "213": "<|6|>", + "214": "<|7|>", + "215": "<|8|>", + "216": "<|9|>", + "217": "<|10|>", + "218": "<|spk0|>", + "219": "<|spk1|>", + "220": "<|spk2|>", + "221": "<|spk3|>", + "222": "<|spk4|>", + "223": "<|spk5|>", + "224": "<|spk6|>", + "225": "<|spk7|>", + "226": "<|spk8|>", + "227": "<|spk9|>", + "228": "<|spk10|>", + "229": "<|spk11|>", + "230": "<|spk12|>", + "231": "<|spk13|>", + "232": "<|spk14|>", + "233": "<|spk15|>", + "234": "0", + "235": "1", + "236": "2", + "237": "3", + "238": "4", + "239": "5", + "240": "6", + "241": "7", + "242": "8", + "243": "9", + "244": "<|spltoken0|>", + "245": "<|spltoken1|>", + "246": "<|spltoken2|>", + "247": "<|spltoken3|>", + "248": "<|spltoken4|>", + "249": "<|spltoken5|>", + "250": "<|spltoken6|>", + "251": "<|spltoken7|>", + "252": "<|spltoken8|>", + "253": "<|spltoken9|>", + "254": "<|spltoken10|>", + "255": "<|spltoken11|>", + "256": "<|spltoken12|>", + "257": "<|spltoken13|>", + "258": "<|spltoken14|>", + "259": "<|spltoken15|>", + "260": "<|spltoken16|>", + "261": "<|spltoken17|>", + "262": "<|spltoken18|>", + "263": "<|spltoken19|>", + "264": "<|spltoken20|>", + "265": "<|spltoken21|>", + "266": "<|spltoken22|>", + "267": "<|spltoken23|>", + "268": "<|spltoken24|>", + "269": "<|spltoken25|>", + "270": "<|spltoken26|>", + "271": "<|spltoken27|>", + "272": "<|spltoken28|>", + "273": "<|spltoken29|>", + "274": "en", + "275": " s", + "276": " d", + "277": "er", + "278": " p", + "279": " a", + "280": " t", + "281": "in", + "282": " m", + "283": "es", + "284": "an", + "285": " n", + "286": " v", + "287": "ar", + "288": " k", + "289": "on", + "290": "is", + "291": "et", + "292": "el", + "293": " l", + "294": "or", + "295": "al", + "296": "at", + "297": "as", + "298": " c", + "299": " o", + "300": " j", + "301": "it", + "302": " b", + "303": " h", + "304": "ie", + "305": "il", + "306": "re", + "307": " u", + "308": "os", + "309": " f", + "310": "ch", + "311": "em", + "312": " i", + "313": "om", + "314": "ra", + "315": "te", + "316": "am", + "317": " g", + "318": " de", + "319": "ad", + "320": "ro", + "321": "ti", + "322": " z", + "323": "us", + "324": "ol", + "325": " e", + "326": "ed", + "327": "ta", + "328": " w", + "329": " п", + "330": "ri", + "331": "un", + "332": "li", + "333": " н", + "334": " in", + "335": "id", + "336": " с", + "337": "le", + "338": " в", + "339": "ak", + "340": "ent", + "341": "qu", + "342": "то", + "343": " S", + "344": "ur", + "345": "ik", + "346": " A", + "347": " se", + "348": "im", + "349": "od", + "350": "ci", + "351": "ut", + "352": " un", + "353": "ov", + "354": " je", + "355": "ul", + "356": " M", + "357": " T", + "358": " na", + "359": "ot", + "360": " D", + "361": " д", + "362": " E", + "363": " qu", + "364": " en", + "365": "est", + "366": " to", + "367": "eg", + "368": "st", + "369": "ai", + "370": "ag", + "371": "ij", + "372": "ра", + "373": " P", + "374": "та", + "375": " r", + "376": "ud", + "377": "ek", + "378": "ig", + "379": " τ", + "380": " I", + "381": "ing", + "382": " N", + "383": "ни", + "384": "av", + "385": "um", + "386": "ко", + "387": "ia", + "388": "he", + "389": "те", + "390": "но", + "391": " на", + "392": "ва", + "393": " la", + "394": "ро", + "395": " з", + "396": "og", + "397": " о", + "398": "ist", + "399": "ir", + "400": " м", + "401": "de", + "402": " и", + "403": "la", + "404": "ic", + "405": " st", + "406": " J", + "407": " at", + "408": " po", + "409": " ne", + "410": " pr", + "411": " an", + "412": "ли", + "413": " al", + "414": "aj", + "415": "ти", + "416": "iv", + "417": " C", + "418": "ре", + "419": "je", + "420": " по", + "421": "ка", + "422": "на", + "423": " es", + "424": "and", + "425": " H", + "426": " B", + "427": " re", + "428": "ου", + "429": "ok", + "430": " K", + "431": " on", + "432": "ne", + "433": " pro", + "434": "to", + "435": " б", + "436": "ab", + "437": " π", + "438": "ou", + "439": " L", + "440": "го", + "441": "ter", + "442": "ien", + "443": " que", + "444": " V", + "445": " da", + "446": "ja", + "447": "na", + "448": "ka", + "449": "ри", + "450": " par", + "451": " su", + "452": " O", + "453": " et", + "454": "ion", + "455": " ja", + "456": "να", + "457": " α", + "458": " do", + "459": " y", + "460": " el", + "461": "ru", + "462": " é", + "463": "ce", + "464": " κ", + "465": " di", + "466": " σ", + "467": "se", + "468": " er", + "469": "ст", + "470": "ma", + "471": " con", + "472": " ε", + "473": "no", + "474": "ap", + "475": " si", + "476": "ен", + "477": "me", + "478": " te", + "479": "ni", + "480": " ч", + "481": " le", + "482": " μ", + "483": " ma", + "484": "än", + "485": " sp", + "486": " за", + "487": "ost", + "488": "end", + "489": "ko", + "490": "are", + "491": " com", + "492": "ke", + "493": " no", + "494": "är", + "495": "ла", + "496": "ers", + "497": " G", + "498": " sa", + "499": " W", + "500": " ir", + "501": "ere", + "502": " vi", + "503": " li", + "504": " не", + "505": " ž", + "506": " the", + "507": "ve", + "508": " is", + "509": " for", + "510": "ob", + "511": "ле", + "512": "во", + "513": "si", + "514": "αι", + "515": " F", + "516": " il", + "517": " U", + "518": " R", + "519": "ть", + "520": "ali", + "521": "ine", + "522": "sa", + "523": " ко", + "524": " š", + "525": " me", + "526": "va", + "527": "ant", + "528": "vo", + "529": "ει", + "530": "ell", + "531": " vo", + "532": "ill", + "533": " det", + "534": "ast", + "535": " ar", + "536": " î", + "537": "ze", + "538": " ra", + "539": "ho", + "540": "ju", + "541": "ör", + "542": "ки", + "543": "ль", + "544": " ni", + "545": "ви", + "546": "ло", + "547": " so", + "548": "ver", + "549": " pre", + "550": " я", + "551": " ta", + "552": " th", + "553": " е", + "554": "tu", + "555": " be", + "556": "да", + "557": "pp", + "558": "ind", + "559": "ge", + "560": " du", + "561": "ess", + "562": "ks", + "563": " od", + "564": "ku", + "565": "ся", + "566": "ac", + "567": "ele", + "568": "ια", + "569": "zy", + "570": "ran", + "571": "ten", + "572": "io", + "573": " în", + "574": "ма", + "575": " and", + "576": "де", + "577": " ka", + "578": " да", + "579": "ди", + "580": "ate", + "581": "iz", + "582": "ite", + "583": "sk", + "584": "ente", + "585": " у", + "586": " za", + "587": " per", + "588": " pri", + "589": " im", + "590": " som", + "591": "da", + "592": " of", + "593": "τα", + "594": " des", + "595": "τι", + "596": "ow", + "597": "ве", + "598": "ny", + "599": " op", + "600": "τε", + "601": " ob", + "602": " den", + "603": " ro", + "604": "ui", + "605": " до", + "606": "az", + "607": " про", + "608": "же", + "609": " ve", + "610": "ão", + "611": " ko", + "612": "iem", + "613": "не", + "614": " ver", + "615": " die", + "616": "ens", + "617": " mo", + "618": " as", + "619": "gy", + "620": "au", + "621": "ort", + "622": " δ", + "623": " der", + "624": " ș", + "625": " van", + "626": " min", + "627": " ch", + "628": "ier", + "629": "lu", + "630": "lo", + "631": " og", + "632": " att", + "633": " то", + "634": " č", + "635": "ji", + "636": "pr", + "637": " var", + "638": "du", + "639": " та", + "640": " tr", + "641": " ab", + "642": " om", + "643": " να", + "644": "що", + "645": "ust", + "646": "gen", + "647": " sk", + "648": "ми", + "649": "ál", + "650": "den", + "651": "ff", + "652": " by", + "653": "mi", + "654": "ás", + "655": "ati", + "656": " се", + "657": " und", + "658": " το", + "659": " um", + "660": "ля", + "661": " est", + "662": "га", + "663": "all", + "664": "ме", + "665": " iz", + "666": "eb", + "667": "ans", + "668": " τη", + "669": "op", + "670": "мо", + "671": " мо", + "672": " от", + "673": "ga", + "674": " mu", + "675": " а", + "676": "ys", + "677": " pas", + "678": " ка", + "679": " nu", + "680": " к", + "681": " Z", + "682": " del", + "683": " mi", + "684": "dz", + "685": "għ", + "686": " tal", + "687": " med", + "688": " tak", + "689": " och", + "690": "kt", + "691": " ac", + "692": " am", + "693": "ní", + "694": " au", + "695": "nd", + "696": "πο", + "697": "ont", + "698": " man", + "699": " pe", + "700": "iti", + "701": " και", + "702": " pod", + "703": "sz", + "704": " și", + "705": "ej", + "706": "ki", + "707": " pl", + "708": " ku", + "709": " av", + "710": " ви", + "711": "ord", + "712": "ha", + "713": " het", + "714": "ada", + "715": "το", + "716": "ρο", + "717": " pos", + "718": " т", + "719": "tä", + "720": "iet", + "721": "cht", + "722": "ale", + "723": "ру", + "724": "ina", + "725": "ck", + "726": "po", + "727": " že", + "728": "wa", + "729": "ρα", + "730": " tu", + "731": "ade", + "732": " nie", + "733": " έ", + "734": " sz", + "735": "ili", + "736": "ba", + "737": "át", + "738": " på", + "739": "pe", + "740": " ό", + "741": "по", + "742": " ра", + "743": " ex", + "744": "ija", + "745": "iss", + "746": " em", + "747": "ala", + "748": "ber", + "749": " ц", + "750": " we", + "751": "gi", + "752": " és", + "753": " ik", + "754": "μα", + "755": "ше", + "756": " os", + "757": " Т", + "758": " kom", + "759": "zi", + "760": " γ", + "761": "ation", + "762": "ond", + "763": "mer", + "764": "yt", + "765": " för", + "766": " sam", + "767": " ap", + "768": " ol", + "769": "ts", + "770": "be", + "771": "ні", + "772": " va", + "773": "ato", + "774": " che", + "775": "че", + "776": "чи", + "777": "za", + "778": " pu", + "779": "хо", + "780": " să", + "781": "ete", + "782": " een", + "783": " għ", + "784": "ät", + "785": "ven", + "786": "for", + "787": "ile", + "788": " az", + "789": "τη", + "790": "bi", + "791": "jo", + "792": " че", + "793": "né", + "794": "tor", + "795": "vi", + "796": "ca", + "797": " ri", + "798": "ți", + "799": " af", + "800": "ons", + "801": "ista", + "802": "äl", + "803": "му", + "804": "nie", + "805": " э", + "806": "до", + "807": "ικ", + "808": " por", + "809": " і", + "810": "mo", + "811": "ug", + "812": " Н", + "813": " he", + "814": "ogy", + "815": " para", + "816": "und", + "817": " das", + "818": " lo", + "819": " εί", + "820": " men", + "821": "és", + "822": " що", + "823": "pl", + "824": "με", + "825": "men", + "826": " ter", + "827": "ня", + "828": " В", + "829": " του", + "830": "ben", + "831": "ны", + "832": " à", + "833": " har", + "834": "се", + "835": " ha", + "836": "bu", + "837": " pol", + "838": " De", + "839": " or", + "840": " ο", + "841": "rav", + "842": " ein", + "843": " при", + "844": "di", + "845": "so", + "846": "за", + "847": "per", + "848": "art", + "849": " pa", + "850": " les", + "851": " vid", + "852": " cu", + "853": " kad", + "854": " vis", + "855": "oll", + "856": " ki", + "857": " är", + "858": "rat", + "859": "ча", + "860": " dat", + "861": "ett", + "862": " что", + "863": "hr", + "864": "che", + "865": " hogy", + "866": "бо", + "867": " co", + "868": "dy", + "869": " it", + "870": "ando", + "871": " En", + "872": " ce", + "873": " ge", + "874": "ung", + "875": " kan", + "876": "сто", + "877": "jen", + "878": " ser", + "879": "ще", + "880": " Ja", + "881": "ih", + "882": " го", + "883": "си", + "884": "ци", + "885": "ara", + "886": " sv", + "887": "ρι", + "888": " με", + "889": " ż", + "890": "ies", + "891": " Se", + "892": " In", + "893": "bl", + "894": "ado", + "895": " jo", + "896": "ult", + "897": "jon", + "898": "res", + "899": "že", + "900": " met", + "901": "der", + "902": "gu", + "903": " til", + "904": " П", + "905": " meg", + "906": " ü", + "907": "ér", + "908": " бу", + "909": " una", + "910": "ram", + "911": "ση", + "912": " ho", + "913": "ym", + "914": "ку", + "915": "ich", + "916": "ore", + "917": "εί", + "918": " из", + "919": "ід", + "920": "án", + "921": " tai", + "922": " С", + "923": "co", + "924": " was", + "925": "ila", + "926": "ement", + "927": "νο", + "928": " kon", + "929": " ist", + "930": "ím", + "931": "pa", + "932": "ela", + "933": " nem", + "934": " val", + "935": "ata", + "936": " tem", + "937": "ang", + "938": " tie", + "939": " pot", + "940": "esti", + "941": "ón", + "942": "int", + "943": "сти", + "944": " Д", + "945": "ста", + "946": "ento", + "947": "elle", + "948": "cz", + "949": " dar", + "950": " г", + "951": "ши", + "952": " пре", + "953": "bo", + "954": "ede", + "955": "red", + "956": " lu", + "957": "πό", + "958": "tr", + "959": "que", + "960": "ler", + "961": "ard", + "962": " Ma", + "963": "ego", + "964": " Al", + "965": "зи", + "966": "ну", + "967": "ini", + "968": "tav", + "969": " ei", + "970": " egy", + "971": " vol", + "972": "amo", + "973": "ib", + "974": "ste", + "975": " об", + "976": "ер", + "977": "ita", + "978": "ana", + "979": "ár", + "980": "жи", + "981": "kl", + "982": " во", + "983": " те", + "984": "ijn", + "985": "лю", + "986": "сь", + "987": "ég", + "988": "ht", + "989": "isk", + "990": " Y", + "991": "nen", + "992": "ega", + "993": " tra", + "994": "ám", + "995": "nos", + "996": "cu", + "997": "ako", + "998": "ave", + "999": " oli", + "1000": " К", + "1001": " kas", + "1002": "go", + "1003": " sem", + "1004": " To", + "1005": "ama", + "1006": "rit", + "1007": "ναι", + "1008": "ning", + "1009": " κα", + "1010": " М", + "1011": " că", + "1012": "ów", + "1013": "jem", + "1014": "ър", + "1015": "ít", + "1016": "kk", + "1017": " jed", + "1018": "ise", + "1019": " си", + "1020": " bi", + "1021": " res", + "1022": "ých", + "1023": " kun", + "1024": " ма", + "1025": " ale", + "1026": "era", + "1027": " mit", + "1028": "las", + "1029": "år", + "1030": " Ne", + "1031": " Je", + "1032": " się", + "1033": " ми", + "1034": "ly", + "1035": " θ", + "1036": " zu", + "1037": " ú", + "1038": " fil", + "1039": "ora", + "1040": "ния", + "1041": "att", + "1042": " bu", + "1043": "olo", + "1044": " dem", + "1045": "ika", + "1046": "ami", + "1047": "ito", + "1048": "eli", + "1049": "ano", + "1050": " that", + "1051": "zt", + "1052": " О", + "1053": "áv", + "1054": "ів", + "1055": "lig", + "1056": " για", + "1057": " př", + "1058": "ние", + "1059": "ду", + "1060": "elt", + "1061": "би", + "1062": " fin", + "1063": " viņ", + "1064": "ία", + "1065": "mu", + "1066": "eda", + "1067": " sig", + "1068": " ħ", + "1069": "υτ", + "1070": "ων", + "1071": "uro", + "1072": "ūs", + "1073": "eren", + "1074": " де", + "1075": "ct", + "1076": " ich", + "1077": "λλ", + "1078": "len", + "1079": "αν", + "1080": "ari", + "1081": " iš", + "1082": "sp", + "1083": "ces", + "1084": " vy", + "1085": "ida", + "1086": "лу", + "1087": " hu", + "1088": " į", + "1089": "ās", + "1090": " uz", + "1091": " inter", + "1092": " ро", + "1093": "agħ", + "1094": "ky", + "1095": "εν", + "1096": "ono", + "1097": "cia", + "1098": "ry", + "1099": "ori", + "1100": " Il", + "1101": " ent", + "1102": " pra", + "1103": " my", + "1104": "ille", + "1105": " Es", + "1106": "ft", + "1107": " dis", + "1108": " pers", + "1109": " είναι", + "1110": " ad", + "1111": " all", + "1112": "ani", + "1113": "unt", + "1114": " Un", + "1115": "τικ", + "1116": "one", + "1117": " tre", + "1118": " fr", + "1119": "ante", + "1120": " И", + "1121": "жа", + "1122": "sen", + "1123": " ότι", + "1124": "oj", + "1125": " это", + "1126": "ві", + "1127": " ke", + "1128": "ār", + "1129": "ane", + "1130": "па", + "1131": "do", + "1132": "ame", + "1133": "äm", + "1134": "ici", + "1135": "ment", + "1136": " cont", + "1137": "со", + "1138": " που", + "1139": "ban", + "1140": "ica", + "1141": " mis", + "1142": "ет", + "1143": "alt", + "1144": "ül", + "1145": "én", + "1146": " è", + "1147": "vat", + "1148": " ty", + "1149": " gr", + "1150": " din", + "1151": "ön", + "1152": " са", + "1153": " από", + "1154": " los", + "1155": " mod", + "1156": "ene", + "1157": " бы", + "1158": "itu", + "1159": " но", + "1160": "led", + "1161": " Det", + "1162": "ού", + "1163": "use", + "1164": " bet", + "1165": " este", + "1166": "ря", + "1167": " зна", + "1168": " την", + "1169": " St", + "1170": " ut", + "1171": "enn", + "1172": " pi", + "1173": " ze", + "1174": "uk", + "1175": "este", + "1176": "zn", + "1177": " mar", + "1178": "ного", + "1179": " ре", + "1180": " you", + "1181": " till", + "1182": " Q", + "1183": "ione", + "1184": "či", + "1185": " han", + "1186": "inu", + "1187": " tas", + "1188": "pu", + "1189": "ink", + "1190": "itt", + "1191": " ak", + "1192": " dan", + "1193": " раз", + "1194": " pour", + "1195": " це", + "1196": " hab", + "1197": " tod", + "1198": "äh", + "1199": "chen", + "1200": "bli", + "1201": "τά", + "1202": "tiv", + "1203": " wir", + "1204": " imp", + "1205": "ice", + "1206": "zz", + "1207": "пи", + "1208": "eri", + "1209": " sch", + "1210": " ti", + "1211": " ju", + "1212": " ot", + "1213": " tik", + "1214": "isi", + "1215": " со", + "1216": "iek", + "1217": "entru", + "1218": " jag", + "1219": "yn", + "1220": " dob", + "1221": "enti", + "1222": "рі", + "1223": " så", + "1224": "ima", + "1225": " χ", + "1226": "ert", + "1227": " raz", + "1228": "ied", + "1229": "wo", + "1230": "ime", + "1231": "ino", + "1232": " pat", + "1233": " że", + "1234": "ρά", + "1235": "ту", + "1236": "ose", + "1237": " kur", + "1238": " pal", + "1239": "her", + "1240": "tion", + "1241": " võ", + "1242": " bil", + "1243": " ва", + "1244": "ουν", + "1245": " No", + "1246": " nek", + "1247": "še", + "1248": "we", + "1249": " inte", + "1250": " ved", + "1251": " sav", + "1252": "ger", + "1253": " Я", + "1254": " see", + "1255": "ken", + "1256": "ši", + "1257": " Ch", + "1258": "ema", + "1259": " А", + "1260": "άν", + "1261": " jeg", + "1262": "pi", + "1263": " fa", + "1264": " mai", + "1265": "eni", + "1266": " ga", + "1267": "ide", + "1268": "сть", + "1269": " ste", + "1270": "пе", + "1271": " συ", + "1272": "les", + "1273": "ux", + "1274": " sta", + "1275": "ін", + "1276": " nad", + "1277": "ės", + "1278": " як", + "1279": "val", + "1280": "ém", + "1281": "emm", + "1282": " uh", + "1283": " br", + "1284": " js", + "1285": " zijn", + "1286": "inen", + "1287": " las", + "1288": "var", + "1289": " care", + "1290": "nu", + "1291": "usi", + "1292": "amente", + "1293": "pre", + "1294": " επ", + "1295": " вы", + "1296": "ää", + "1297": "unk", + "1298": "ike", + "1299": "het", + "1300": " ten", + "1301": " sie", + "1302": "ться", + "1303": "ía", + "1304": "ür", + "1305": " An", + "1306": " dom", + "1307": "tig", + "1308": "ser", + "1309": " La", + "1310": "tre", + "1311": "amos", + "1312": " τα", + "1313": " tro", + "1314": " non", + "1315": " q", + "1316": " fi", + "1317": "man", + "1318": "ги", + "1319": " pentru", + "1320": "ισ", + "1321": " bo", + "1322": " съ", + "1323": "ρε", + "1324": " η", + "1325": "up", + "1326": " kol", + "1327": "ži", + "1328": "ām", + "1329": " aby", + "1330": " gen", + "1331": " tam", + "1332": "ova", + "1333": " Le", + "1334": "ese", + "1335": " Α", + "1336": " wh", + "1337": "ove", + "1338": "ολ", + "1339": " Sie", + "1340": " β", + "1341": "лі", + "1342": " ф", + "1343": "imo", + "1344": "vis", + "1345": " une", + "1346": "ρό", + "1347": " wa", + "1348": "ně", + "1349": "ott", + "1350": " jak", + "1351": " бе", + "1352": " fu", + "1353": "eta", + "1354": "van", + "1355": "ét", + "1356": "uma", + "1357": " nä", + "1358": "uj", + "1359": " Vi", + "1360": "esta", + "1361": " fo", + "1362": " ikke", + "1363": " kien", + "1364": "mente", + "1365": " може", + "1366": " ни", + "1367": "ums", + "1368": " että", + "1369": " hän", + "1370": "ije", + "1371": " nos", + "1372": "ige", + "1373": "āk", + "1374": " как", + "1375": " vien", + "1376": "sta", + "1377": " mil", + "1378": "ero", + "1379": "ці", + "1380": " reg", + "1381": "lj", + "1382": " Qu", + "1383": "ого", + "1384": "oli", + "1385": " jest", + "1386": "cie", + "1387": "esto", + "1388": " over", + "1389": "af", + "1390": " хо", + "1391": " φ", + "1392": " αυτ", + "1393": " στο", + "1394": "joni", + "1395": " dru", + "1396": "ται", + "1397": " aut", + "1398": "σε", + "1399": " voor", + "1400": " mal", + "1401": "pt", + "1402": "ras", + "1403": "ån", + "1404": "eme", + "1405": "ła", + "1406": "cion", + "1407": " Na", + "1408": " vous", + "1409": " wer", + "1410": "urs", + "1411": "ør", + "1412": "ры", + "1413": "ick", + "1414": "cê", + "1415": " nas", + "1416": " ö", + "1417": "ção", + "1418": " kr", + "1419": "ena", + "1420": "ха", + "1421": " uma", + "1422": " vai", + "1423": "hom", + "1424": " ind", + "1425": " все", + "1426": " zn", + "1427": " como", + "1428": "ins", + "1429": "ende", + "1430": " mor", + "1431": "ере", + "1432": " това", + "1433": " als", + "1434": "ταν", + "1435": "ely", + "1436": " svo", + "1437": " ud", + "1438": "tó", + "1439": " vel", + "1440": "пра", + "1441": " us", + "1442": "zen", + "1443": "hn", + "1444": "ción", + "1445": " Б", + "1446": " vil", + "1447": "ps", + "1448": " naj", + "1449": "une", + "1450": " yra", + "1451": " x", + "1452": " roz", + "1453": "мен", + "1454": " ktor", + "1455": " go", + "1456": "wi", + "1457": "aks", + "1458": "ella", + "1459": " probl", + "1460": "ity", + "1461": " θα", + "1462": " bar", + "1463": "са", + "1464": "σει", + "1465": " mais", + "1466": " αν", + "1467": " То", + "1468": " sl", + "1469": " comp", + "1470": " não", + "1471": "wn", + "1472": "ive", + "1473": " von", + "1474": " qui", + "1475": "vel", + "1476": "lich", + "1477": "nosti", + "1478": "min", + "1479": " war", + "1480": "uje", + "1481": " niet", + "1482": " ή", + "1483": "ιο", + "1484": "iau", + "1485": "ään", + "1486": "ým", + "1487": "μέ", + "1488": "ament", + "1489": "ole", + "1490": " sin", + "1491": " not", + "1492": " Ar", + "1493": " pom", + "1494": " vor", + "1495": "āt", + "1496": "por", + "1497": " bud", + "1498": " wy", + "1499": "ба", + "1500": "isti", + "1501": " aj", + "1502": " The", + "1503": " tur", + "1504": " sur", + "1505": "ят", + "1506": "jet", + "1507": "gan", + "1508": " Ε", + "1509": " kor", + "1510": "iex", + "1511": " nicht", + "1512": " dal", + "1513": "ρί", + "1514": "eld", + "1515": "іль", + "1516": "let", + "1517": " kar", + "1518": " dr", + "1519": "uri", + "1520": "by", + "1521": "iden", + "1522": "ern", + "1523": " ako", + "1524": " σε", + "1525": "că", + "1526": "bil", + "1527": " dans", + "1528": "utta", + "1529": "asta", + "1530": " sm", + "1531": "ret", + "1532": " λ", + "1533": "ido", + "1534": "age", + "1535": " her", + "1536": " προ", + "1537": " mas", + "1538": "inn", + "1539": " бо", + "1540": " dass", + "1541": " пра", + "1542": "kom", + "1543": " З", + "1544": " mat", + "1545": " kā", + "1546": " El", + "1547": " від", + "1548": "ší", + "1549": " wie", + "1550": "pro", + "1551": " tar", + "1552": " tud", + "1553": " ca", + "1554": "kon", + "1555": " πε", + "1556": " па", + "1557": "lim", + "1558": " bes", + "1559": " kai", + "1560": "ros", + "1561": " для", + "1562": " pred", + "1563": "ву", + "1564": "νε", + "1565": " era", + "1566": " ave", + "1567": " bū", + "1568": "nik", + "1569": "ith", + "1570": " któ", + "1571": "ors", + "1572": "щи", + "1573": " ста", + "1574": " tom", + "1575": " mus", + "1576": " gal", + "1577": "cer", + "1578": "tt", + "1579": " Š", + "1580": " leg", + "1581": "rij", + "1582": " Τ", + "1583": " Euro", + "1584": " Tai", + "1585": "form", + "1586": " ст", + "1587": " ме", + "1588": " div", + "1589": "ον", + "1590": " ду", + "1591": "ска", + "1592": " fl", + "1593": " auf", + "1594": " cons", + "1595": " ci", + "1596": " так", + "1597": "ket", + "1598": " kö", + "1599": " ali", + "1600": " dos", + "1601": "ουμε", + "1602": "vad", + "1603": " str", + "1604": "itä", + "1605": "ase", + "1606": " nog", + "1607": "ann", + "1608": "лен", + "1609": "fic", + "1610": " υ", + "1611": "fer", + "1612": " Κ", + "1613": " сво", + "1614": "elo", + "1615": "ec", + "1616": "pos", + "1617": " sen", + "1618": " nous", + "1619": " Com", + "1620": " ли", + "1621": "зо", + "1622": " sim", + "1623": " más", + "1624": " δεν", + "1625": " Po", + "1626": "īb", + "1627": " gl", + "1628": " prog", + "1629": "ekk", + "1630": "ure", + "1631": " πα", + "1632": "ски", + "1633": " sc", + "1634": " tri", + "1635": " sob", + "1636": " kui", + "1637": " ag", + "1638": "kun", + "1639": " bl", + "1640": " ще", + "1641": "ών", + "1642": " hm", + "1643": "ρέ", + "1644": "cy", + "1645": "tim", + "1646": " ji", + "1647": " have", + "1648": "alo", + "1649": " ole", + "1650": "ól", + "1651": "vet", + "1652": "rt", + "1653": " Tak", + "1654": " person", + "1655": " ча", + "1656": " tagħ", + "1657": "asi", + "1658": " στη", + "1659": "šan", + "1660": "zd", + "1661": " fel", + "1662": " inc", + "1663": "EE", + "1664": "λο", + "1665": " ένα", + "1666": " ве", + "1667": " você", + "1668": " mig", + "1669": " už", + "1670": "ща", + "1671": " Mar", + "1672": "ág", + "1673": " heb", + "1674": "ēs", + "1675": " vä", + "1676": " Da", + "1677": " bol", + "1678": " ih", + "1679": "ści", + "1680": "vor", + "1681": "št", + "1682": "λα", + "1683": " sus", + "1684": "ker", + "1685": " cs", + "1686": " kter", + "1687": "uld", + "1688": "eks", + "1689": " nes", + "1690": "сно", + "1691": "bb", + "1692": "ava", + "1693": " alle", + "1694": " Par", + "1695": "war", + "1696": "ēj", + "1697": "iko", + "1698": "rad", + "1699": "nost", + "1700": " pla", + "1701": "yl", + "1702": "par", + "1703": "ovan", + "1704": "ân", + "1705": " dé", + "1706": " vers", + "1707": "nak", + "1708": " vide", + "1709": " eu", + "1710": "ette", + "1711": " cer", + "1712": "lik", + "1713": "мі", + "1714": " are", + "1715": "oni", + "1716": "ák", + "1717": " ou", + "1718": " pov", + "1719": "hed", + "1720": " ah", + "1721": "gel", + "1722": "hen", + "1723": "ło", + "1724": "ак", + "1725": "hu", + "1726": "ний", + "1727": "ün", + "1728": "ss", + "1729": " don", + "1730": "це", + "1731": " hat", + "1732": "κε", + "1733": " х", + "1734": " nov", + "1735": "hat", + "1736": "imi", + "1737": " mind", + "1738": " tiegħ", + "1739": " buvo", + "1740": " inv", + "1741": "oc", + "1742": "ner", + "1743": " car", + "1744": "λε", + "1745": "él", + "1746": " produ", + "1747": "kol", + "1748": "ados", + "1749": " tä", + "1750": "fter", + "1751": " gro", + "1752": " Be", + "1753": "nej", + "1754": "yst", + "1755": "almente", + "1756": " Σ", + "1757": " plus", + "1758": " Pro", + "1759": "жно", + "1760": "áz", + "1761": "ekt", + "1762": "бе", + "1763": " mer", + "1764": "nes", + "1765": " pie", + "1766": " τι", + "1767": "asti", + "1768": "ните", + "1769": " Te", + "1770": "agy", + "1771": " ć", + "1772": " mog", + "1773": " ба", + "1774": "ulle", + "1775": "its", + "1776": "ativ", + "1777": "oti", + "1778": "my", + "1779": " τον", + "1780": "ats", + "1781": "льно", + "1782": " По", + "1783": "ona", + "1784": " pres", + "1785": "kel", + "1786": " кото", + "1787": " Ale", + "1788": " aan", + "1789": " пере", + "1790": "rem", + "1791": " wi", + "1792": "ingap", + "1793": "су", + "1794": "āj", + "1795": "ità", + "1796": " id", + "1797": "yv", + "1798": " Е", + "1799": "ingen", + "1800": " мен", + "1801": "лед", + "1802": "ater", + "1803": "ue", + "1804": " tran", + "1805": " Me", + "1806": "σι", + "1807": " oma", + "1808": "ilo", + "1809": " Π", + "1810": " sol", + "1811": " dig", + "1812": "ство", + "1813": " Jo", + "1814": "ор", + "1815": "ah", + "1816": " eller", + "1817": " Sch", + "1818": "ша", + "1819": "oč", + "1820": " μα", + "1821": " под", + "1822": "oz", + "1823": "λη", + "1824": " prin", + "1825": " kl", + "1826": "τή", + "1827": " лю", + "1828": "ско", + "1829": " biex", + "1830": " eine", + "1831": " ré", + "1832": "чно", + "1833": " dz", + "1834": "emos", + "1835": "akt", + "1836": "ær", + "1837": " dire", + "1838": "ass", + "1839": " għal", + "1840": " up", + "1841": "emb", + "1842": " We", + "1843": " dol", + "1844": "jer", + "1845": " έχ", + "1846": "μο", + "1847": "ura", + "1848": " bija", + "1849": " mon", + "1850": " lai", + "1851": " На", + "1852": "esso", + "1853": " Er", + "1854": "cij", + "1855": "iste", + "1856": " én", + "1857": " nap", + "1858": "šk", + "1859": "гу", + "1860": "ysl", + "1861": "сте", + "1862": "ého", + "1863": "ěl", + "1864": " kap", + "1865": "anti", + "1866": " cor", + "1867": "kr", + "1868": " with", + "1869": " Μ", + "1870": "ong", + "1871": " cre", + "1872": "зу", + "1873": "ande", + "1874": "ív", + "1875": " rec", + "1876": " He", + "1877": "ργ", + "1878": " star", + "1879": "νη", + "1880": " zd", + "1881": " tā", + "1882": "czy", + "1883": "hod", + "1884": " ny", + "1885": "cs", + "1886": " cz", + "1887": "ald", + "1888": " У", + "1889": " što", + "1890": "ný", + "1891": "ēr", + "1892": "pli", + "1893": " poz", + "1894": " pop", + "1895": " int", + "1896": "ach", + "1897": "ijiet", + "1898": "anto", + "1899": " Ich", + "1900": " sich", + "1901": "ome", + "1902": "enta", + "1903": " come", + "1904": " kä", + "1905": " sab", + "1906": " bli", + "1907": " app", + "1908": "ád", + "1909": " pon", + "1910": "ем", + "1911": "elen", + "1912": "vol", + "1913": " przy", + "1914": " naš", + "1915": "ais", + "1916": "ând", + "1917": " cam", + "1918": "ння", + "1919": " lah", + "1920": " Ik", + "1921": "åg", + "1922": " Oh", + "1923": " had", + "1924": " mag", + "1925": "rie", + "1926": " κά", + "1927": "λλά", + "1928": " nav", + "1929": "ły", + "1930": "ρω", + "1931": "ρη", + "1932": " mill", + "1933": "pri", + "1934": " kont", + "1935": "ří", + "1936": " nor", + "1937": " Men", + "1938": " esta", + "1939": " zo", + "1940": " maj", + "1941": "вер", + "1942": " ru", + "1943": "des", + "1944": " zap", + "1945": " sag", + "1946": " pou", + "1947": " дру", + "1948": " Ta", + "1949": " би", + "1950": " Ο", + "1951": "fi", + "1952": " für", + "1953": " vý", + "1954": "ėl", + "1955": " aus", + "1956": "yk", + "1957": " Jag", + "1958": "fe", + "1959": " ά", + "1960": " So", + "1961": " kell", + "1962": "eti", + "1963": "ull", + "1964": "зна", + "1965": " dit", + "1966": " пред", + "1967": "anie", + "1968": "κα", + "1969": " gra", + "1970": "ties", + "1971": " sel", + "1972": "ams", + "1973": "mis", + "1974": " this", + "1975": "endo", + "1976": " And", + "1977": " pak", + "1978": " zá", + "1979": "hi", + "1980": " gu", + "1981": " ur", + "1982": "hd", + "1983": " ger", + "1984": " nat", + "1985": " tot", + "1986": "ení", + "1987": " μια", + "1988": "olog", + "1989": " й", + "1990": " Re", + "1991": " hal", + "1992": "zu", + "1993": "ijo", + "1994": " vir", + "1995": " pad", + "1996": " αυτό", + "1997": "una", + "1998": "su", + "1999": "net", + "2000": "пу", + "2001": " fra", + "2002": "mes", + "2003": "land", + "2004": " мы", + "2005": " Vo", + "2006": "ді", + "2007": "osa", + "2008": " É", + "2009": " out", + "2010": "osta", + "2011": " Nu", + "2012": "kö", + "2013": "ades", + "2014": "iten", + "2015": "oma", + "2016": " ie", + "2017": " kõ", + "2018": "cis", + "2019": " let", + "2020": " huwa", + "2021": " kam", + "2022": " vr", + "2023": " cas", + "2024": " sal", + "2025": " god", + "2026": "ová", + "2027": " Ко", + "2028": "anje", + "2029": "wie", + "2030": "esz", + "2031": "lä", + "2032": " está", + "2033": " sono", + "2034": " mes", + "2035": "вати", + "2036": "ului", + "2037": "mas", + "2038": "yd", + "2039": "ъл", + "2040": " ker", + "2041": "ev", + "2042": " има", + "2043": " fost", + "2044": "als", + "2045": " или", + "2046": "гра", + "2047": " cel", + "2048": " просто", + "2049": " Р", + "2050": " Pr", + "2051": " mom", + "2052": "ēl", + "2053": " kuri", + "2054": "xx", + "2055": " tö", + "2056": "oka", + "2057": " bez", + "2058": "жу", + "2059": " can", + "2060": " ide", + "2061": " má", + "2062": "ná", + "2063": "ii", + "2064": "ça", + "2065": "iku", + "2066": "лко", + "2067": " Ha", + "2068": "ната", + "2069": " ш", + "2070": " dok", + "2071": " Singap", + "2072": "сі", + "2073": "στε", + "2074": "ych", + "2075": "ény", + "2076": "gs", + "2077": "ēt", + "2078": " ő", + "2079": "nih", + "2080": " δια", + "2081": "oro", + "2082": " Het", + "2083": " Jeg", + "2084": "mit", + "2085": "ew", + "2086": "bar", + "2087": "udz", + "2088": " wor", + "2089": "ът", + "2090": " oc", + "2091": "ći", + "2092": " lab", + "2093": "ico", + "2094": " жи", + "2095": "ores", + "2096": " ez", + "2097": " ov", + "2098": " ї", + "2099": "ble", + "2100": "jud", + "2101": "elj", + "2102": " ai", + "2103": "cent", + "2104": " neg", + "2105": " parti", + "2106": " Die", + "2107": "rol", + "2108": " minn", + "2109": " μπο", + "2110": " tut", + "2111": "ρχ", + "2112": " cap", + "2113": "гда", + "2114": " volt", + "2115": " Но", + "2116": " prof", + "2117": " seda", + "2118": "sch", + "2119": " pen", + "2120": "бы", + "2121": " gy", + "2122": "ăr", + "2123": "дно", + "2124": "rov", + "2125": "ania", + "2126": " beg", + "2127": "umu", + "2128": " col", + "2129": " Г", + "2130": "rig", + "2131": "isen", + "2132": " kat", + "2133": "lev", + "2134": " ett", + "2135": " ně", + "2136": " nep", + "2137": "ék", + "2138": " parte", + "2139": "pen", + "2140": " ġ", + "2141": " polit", + "2142": "iej", + "2143": "ād", + "2144": "ty", + "2145": "ρώ", + "2146": "br", + "2147": " vš", + "2148": " ok", + "2149": "бу", + "2150": " son", + "2151": "σα", + "2152": "ată", + "2153": "θε", + "2154": "ajo", + "2155": " ξ", + "2156": " sú", + "2157": "ιά", + "2158": " væ", + "2159": " väl", + "2160": " kot", + "2161": " alla", + "2162": " jako", + "2163": "lad", + "2164": " fac", + "2165": "ħal", + "2166": " fuq", + "2167": " nar", + "2168": " še", + "2169": " мі", + "2170": " jsem", + "2171": " živ", + "2172": "issa", + "2173": " μέ", + "2174": " prze", + "2175": "del", + "2176": " Is", + "2177": " ele", + "2178": " dur", + "2179": " като", + "2180": "szy", + "2181": " Ir", + "2182": " inst", + "2183": "жда", + "2184": "чен", + "2185": " ned", + "2186": "afna", + "2187": "ola", + "2188": "īt", + "2189": "ère", + "2190": "igen", + "2191": " ná", + "2192": "ank", + "2193": " import", + "2194": " ну", + "2195": "gl", + "2196": "πε", + "2197": " є", + "2198": " mõ", + "2199": "osti", + "2200": "об", + "2201": " των", + "2202": "ете", + "2203": "ovor", + "2204": "uv", + "2205": "els", + "2206": "eto", + "2207": " rel", + "2208": "ют", + "2209": "emo", + "2210": " Th", + "2211": " οι", + "2212": "eno", + "2213": " teg", + "2214": "uni", + "2215": " tako", + "2216": " cent", + "2217": "ité", + "2218": "cul", + "2219": " ont", + "2220": " tag", + "2221": " Et", + "2222": " kes", + "2223": "cho", + "2224": "tak", + "2225": "isu", + "2226": "nek", + "2227": " ин", + "2228": "ument", + "2229": "гато", + "2230": "каза", + "2231": " sind", + "2232": "дин", + "2233": " Så", + "2234": "τό", + "2235": " inf", + "2236": "ца", + "2237": "áll", + "2238": "тел", + "2239": " Č", + "2240": " Han", + "2241": "про", + "2242": "taa", + "2243": " smo", + "2244": " lui", + "2245": "ute", + "2246": " diff", + "2247": " part", + "2248": "bre", + "2249": "oks", + "2250": "ēc", + "2251": " ig", + "2252": " qed", + "2253": " кон", + "2254": "τέ", + "2255": " pār", + "2256": " spe", + "2257": "ті", + "2258": "ġġ", + "2259": "eten", + "2260": " много", + "2261": " tun", + "2262": " tv", + "2263": " alg", + "2264": "ée", + "2265": "ance", + "2266": "nych", + "2267": "aju", + "2268": " mol", + "2269": " sobre", + "2270": "lus", + "2271": " pok", + "2272": " tim", + "2273": "ány", + "2274": "ally", + "2275": "isten", + "2276": " må", + "2277": " Ц", + "2278": " kommer", + "2279": " μου", + "2280": "kov", + "2281": " has", + "2282": " ¿", + "2283": "onom", + "2284": " uit", + "2285": "αα", + "2286": "ns", + "2287": " πολ", + "2288": "iec", + "2289": " dv", + "2290": " jūs", + "2291": " vagy", + "2292": "ść", + "2293": " Э", + "2294": "old", + "2295": " въ", + "2296": "cial", + "2297": " роз", + "2298": " ти", + "2299": "ire", + "2300": "ip", + "2301": "erg", + "2302": "iks", + "2303": " vl", + "2304": " mūs", + "2305": " bij", + "2306": " εν", + "2307": " dies", + "2308": " isso", + "2309": "ijk", + "2310": " mul", + "2311": " στην", + "2312": " Ez", + "2313": " lahko", + "2314": " mož", + "2315": " Du", + "2316": "hl", + "2317": "ět", + "2318": " Не", + "2319": " Ș", + "2320": "gt", + "2321": " ví", + "2322": "zer", + "2323": " ska", + "2324": " dra", + "2325": " seg", + "2326": " Х", + "2327": "our", + "2328": " geb", + "2329": " sunt", + "2330": " nam", + "2331": "owa", + "2332": " tek", + "2333": "ved", + "2334": "ства", + "2335": "ões", + "2336": " It", + "2337": " gran", + "2338": " program", + "2339": " ant", + "2340": "aire", + "2341": "ност", + "2342": " Si", + "2343": " See", + "2344": "σω", + "2345": "vu", + "2346": " end", + "2347": " hij", + "2348": " Έ", + "2349": "lij", + "2350": "iał", + "2351": " Î", + "2352": "iment", + "2353": " ħafna", + "2354": "rod", + "2355": " ass", + "2356": "aliz", + "2357": "mel", + "2358": "ята", + "2359": " fol", + "2360": "sti", + "2361": "vid", + "2362": " haben", + "2363": " Mo", + "2364": " della", + "2365": " più", + "2366": "forma", + "2367": "ös", + "2368": " Man", + "2369": "ude", + "2370": "ligt", + "2371": "ето", + "2372": " απο", + "2373": "μά", + "2374": "met", + "2375": "emp", + "2376": "assa", + "2377": "cí", + "2378": " нас", + "2379": "жен", + "2380": " upp", + "2381": " kuid", + "2382": "inā", + "2383": " Η", + "2384": " poč", + "2385": "pad", + "2386": "pres", + "2387": "lar", + "2388": " fam", + "2389": "сли", + "2390": "yb", + "2391": "aka", + "2392": "mar", + "2393": " taip", + "2394": "ран", + "2395": "πα", + "2396": "τί", + "2397": "них", + "2398": "ets", + "2399": "ings", + "2400": "avo", + "2401": "οι", + "2402": " finan", + "2403": "yti", + "2404": "ition", + "2405": " As", + "2406": "uks", + "2407": " περι", + "2408": "ämä", + "2409": " alt", + "2410": " ris", + "2411": "té", + "2412": "ira", + "2413": " Und", + "2414": "ton", + "2415": " lä", + "2416": "ren", + "2417": "ider", + "2418": "вы", + "2419": " Og", + "2420": " gan", + "2421": "aten", + "2422": "čno", + "2423": " efter", + "2424": " спо", + "2425": " fe", + "2426": " dev", + "2427": "now", + "2428": "entes", + "2429": "ные", + "2430": " vez", + "2431": "usta", + "2432": "uto", + "2433": " rad", + "2434": " pun", + "2435": "koll", + "2436": " Est", + "2437": " dost", + "2438": "спо", + "2439": " akt", + "2440": " oh", + "2441": " вре", + "2442": " under", + "2443": "зва", + "2444": " Л", + "2445": " Im", + "2446": " sku", + "2447": "кий", + "2448": " sil", + "2449": " которы", + "2450": " sua", + "2451": "ars", + "2452": " Да", + "2453": " ost", + "2454": "ээ", + "2455": "elu", + "2456": "ria", + "2457": " hel", + "2458": " kos", + "2459": " kaip", + "2460": " għand", + "2461": "ioni", + "2462": "jih", + "2463": " ня", + "2464": " net", + "2465": " pap", + "2466": " prost", + "2467": "ты", + "2468": "ють", + "2469": " mēs", + "2470": " här", + "2471": "tek", + "2472": " Am", + "2473": "igt", + "2474": "vē", + "2475": "üü", + "2476": " jos", + "2477": "tait", + "2478": " І", + "2479": " hav", + "2480": "шо", + "2481": "reb", + "2482": " pag", + "2483": "ходи", + "2484": "rid", + "2485": " naar", + "2486": " prav", + "2487": "orn", + "2488": " mot", + "2489": "ίν", + "2490": "cha", + "2491": "ral", + "2492": " continu", + "2493": "ld", + "2494": " far", + "2495": " stud", + "2496": " ess", + "2497": " Ver", + "2498": " он", + "2499": "ības", + "2500": "ėj", + "2501": "ėjo", + "2502": "kin", + "2503": " kra", + "2504": " avec", + "2505": "εια", + "2506": "raz", + "2507": " problem", + "2508": " ήταν", + "2509": "σο", + "2510": "pet", + "2511": "ръ", + "2512": "γμα", + "2513": " organ", + "2514": " vu", + "2515": "ils", + "2516": "чу", + "2517": "azz", + "2518": "дна", + "2519": " pä", + "2520": " len", + "2521": " рабо", + "2522": "če", + "2523": "ätt", + "2524": "ρεί", + "2525": " werden", + "2526": "ume", + "2527": "ито", + "2528": "arb", + "2529": " jis", + "2530": " they", + "2531": "ín", + "2532": " dec", + "2533": "вори", + "2534": " сто", + "2535": " daug", + "2536": " plan", + "2537": " savo", + "2538": "illa", + "2539": " maar", + "2540": "ès", + "2541": " vad", + "2542": "ilor", + "2543": "ub", + "2544": " qual", + "2545": "gal", + "2546": "hal", + "2547": "вно", + "2548": "кти", + "2549": " су", + "2550": " ди", + "2551": "ack", + "2552": " koji", + "2553": "ική", + "2554": "вен", + "2555": " wo", + "2556": " also", + "2557": " någ", + "2558": "lan", + "2559": " За", + "2560": " кра", + "2561": " są", + "2562": " Ч", + "2563": " Den", + "2564": " sme", + "2565": " bed", + "2566": " він", + "2567": " zv", + "2568": "вя", + "2569": "get", + "2570": " Viņ", + "2571": "osi", + "2572": "cip", + "2573": " під", + "2574": " stran", + "2575": " hun", + "2576": " tid", + "2577": " Bet", + "2578": "jeg", + "2579": " õ", + "2580": "cess", + "2581": "raw", + "2582": "ден", + "2583": " които", + "2584": "ных", + "2585": " Kom", + "2586": "mon", + "2587": "oda", + "2588": " Och", + "2589": "irm", + "2590": "jn", + "2591": "ép", + "2592": "area", + "2593": "θη", + "2594": " bien", + "2595": " huma", + "2596": "ovi", + "2597": " kok", + "2598": "essa", + "2599": "ša", + "2600": " ši", + "2601": " hebben", + "2602": " ba", + "2603": "bol", + "2604": " mak", + "2605": " ști", + "2606": "ska", + "2607": "ész", + "2608": "čin", + "2609": " така", + "2610": "hm", + "2611": " prz", + "2612": " sein", + "2613": " siis", + "2614": "vil", + "2615": "ją", + "2616": "λά", + "2617": " put", + "2618": "ua", + "2619": " Ν", + "2620": "ünk", + "2621": " situ", + "2622": "ей", + "2623": "пре", + "2624": " ін", + "2625": " sell", + "2626": "alla", + "2627": "yslím", + "2628": " sau", + "2629": " ле", + "2630": "wer", + "2631": "isto", + "2632": "pod", + "2633": " enc", + "2634": " New", + "2635": " čas", + "2636": "rar", + "2637": "if", + "2638": "rá", + "2639": " bilo", + "2640": "šu", + "2641": " ven", + "2642": "ee", + "2643": "ρού", + "2644": " буде", + "2645": "тя", + "2646": "leg", + "2647": "esi", + "2648": " му", + "2649": " num", + "2650": "πά", + "2651": " pens", + "2652": " hija", + "2653": " samo", + "2654": " selle", + "2655": " sec", + "2656": " like", + "2657": " bom", + "2658": "elf", + "2659": "вля", + "2660": "ért", + "2661": " veľ", + "2662": "tų", + "2663": "vit", + "2664": " Δ", + "2665": "pon", + "2666": "ță", + "2667": "ük", + "2668": " беше", + "2669": "ât", + "2670": "fin", + "2671": " szer", + "2672": " πο", + "2673": "any", + "2674": " Nie", + "2675": "iga", + "2676": " uns", + "2677": " gru", + "2678": " one", + "2679": " пос", + "2680": "χε", + "2681": " but", + "2682": "jed", + "2683": "isa", + "2684": " jot", + "2685": "йте", + "2686": " kön", + "2687": " quando", + "2688": "oi", + "2689": " kal", + "2690": " wat", + "2691": " nost", + "2692": " post", + "2693": " Um", + "2694": " sul", + "2695": " nagy", + "2696": " xi", + "2697": " waar", + "2698": "los", + "2699": "sten", + "2700": "esse", + "2701": " tap", + "2702": " sak", + "2703": "anja", + "2704": " Ze", + "2705": "amento", + "2706": "πει", + "2707": "ają", + "2708": " gre", + "2709": " questo", + "2710": "și", + "2711": " Ali", + "2712": " ку", + "2713": " vet", + "2714": "uden", + "2715": " sn", + "2716": "idu", + "2717": " nej", + "2718": "dzie", + "2719": "ís", + "2720": "ivers", + "2721": " pog", + "2722": " čo", + "2723": "izz", + "2724": "tai", + "2725": " εκ", + "2726": " ljud", + "2727": " će", + "2728": "τικό", + "2729": " form", + "2730": " taj", + "2731": "cio", + "2732": "indi", + "2733": "ll", + "2734": " sub", + "2735": " чтобы", + "2736": "vez", + "2737": " gar", + "2738": " най", + "2739": "ish", + "2740": " nik", + "2741": "istä", + "2742": "erd", + "2743": " vz", + "2744": "ez", + "2745": " his", + "2746": "нь", + "2747": "oval", + "2748": "λή", + "2749": " επι", + "2750": "inti", + "2751": "ій", + "2752": " arī", + "2753": " sont", + "2754": " щоб", + "2755": " prie", + "2756": " liv", + "2757": " tõ", + "2758": " moż", + "2759": " стра", + "2760": "avad", + "2761": " ben", + "2762": "ident", + "2763": " lid", + "2764": "īdz", + "2765": " чи", + "2766": "ales", + "2767": "iter", + "2768": "kal", + "2769": "odu", + "2770": " Și", + "2771": " pass", + "2772": "ване", + "2773": "ony", + "2774": "uste", + "2775": "emme", + "2776": "idad", + "2777": " пла", + "2778": "jat", + "2779": "при", + "2780": "miss", + "2781": " kv", + "2782": "ski", + "2783": " will", + "2784": " hand", + "2785": "tra", + "2786": " дума", + "2787": " σα", + "2788": " stor", + "2789": "fa", + "2790": " bel", + "2791": "ăm", + "2792": "кла", + "2793": " għall", + "2794": " hi", + "2795": " tele", + "2796": "ché", + "2797": " kdy", + "2798": "EEEE", + "2799": "ється", + "2800": " did", + "2801": " Takže", + "2802": " dist", + "2803": "λέ", + "2804": "дя", + "2805": "ный", + "2806": "ovat", + "2807": "ós", + "2808": "ții", + "2809": "cija", + "2810": "tic", + "2811": " dej", + "2812": "вите", + "2813": " cr", + "2814": "itet", + "2815": " chi", + "2816": "zione", + "2817": " sed", + "2818": "uss", + "2819": "nud", + "2820": " dag", + "2821": "ations", + "2822": "seb", + "2823": " dov", + "2824": " tan", + "2825": "cep", + "2826": "ție", + "2827": "ίζ", + "2828": " gi", + "2829": "čen", + "2830": " kako", + "2831": "ωω", + "2832": "δο", + "2833": " solo", + "2834": "waż", + "2835": " Ho", + "2836": "iska", + "2837": " spr", + "2838": " né", + "2839": "yr", + "2840": "φο", + "2841": "mod", + "2842": "nom", + "2843": "kas", + "2844": " Sp", + "2845": "hte", + "2846": " just", + "2847": "ċċ", + "2848": "āju", + "2849": " hist", + "2850": " å", + "2851": "ват", + "2852": " faz", + "2853": "onn", + "2854": " тако", + "2855": "он", + "2856": " его", + "2857": " ś", + "2858": " vra", + "2859": " ανα", + "2860": " skulle", + "2861": " mě", + "2862": " aqu", + "2863": " bra", + "2864": "vä", + "2865": " know", + "2866": "lio", + "2867": "ula", + "2868": "aja", + "2869": "сту", + "2870": " On", + "2871": " Co", + "2872": " cin", + "2873": "alis", + "2874": " hier", + "2875": "vir", + "2876": " asi", + "2877": "īj", + "2878": " já", + "2879": " hát", + "2880": " sor", + "2881": "iva", + "2882": " gener", + "2883": " mond", + "2884": "ότε", + "2885": "lat", + "2886": "ним", + "2887": " tul", + "2888": " hemm", + "2889": "κό", + "2890": "ild", + "2891": " πολύ", + "2892": " door", + "2893": "esp", + "2894": " ook", + "2895": " joka", + "2896": " sou", + "2897": "bor", + "2898": "zą", + "2899": " ком", + "2900": "esch", + "2901": "ator", + "2902": " esc", + "2903": "пер", + "2904": " wię", + "2905": " Hän", + "2906": "γο", + "2907": " ä", + "2908": "ovo", + "2909": " În", + "2910": " же", + "2911": " nev", + "2912": "hekk", + "2913": " zna", + "2914": " Fran", + "2915": "hing", + "2916": " ace", + "2917": " EU", + "2918": " dei", + "2919": "νέ", + "2920": "ől", + "2921": " Це", + "2922": " zač", + "2923": " år", + "2924": "gli", + "2925": "кра", + "2926": " ту", + "2927": " nii", + "2928": " prom", + "2929": " hom", + "2930": "uta", + "2931": "ется", + "2932": " Az", + "2933": "чки", + "2934": "rop", + "2935": "arte", + "2936": " eff", + "2937": "ħħ", + "2938": "être", + "2939": "sto", + "2940": " Та", + "2941": " vend", + "2942": " mida", + "2943": " bas", + "2944": " über", + "2945": " Mas", + "2946": " ed", + "2947": " več", + "2948": "ungen", + "2949": "τικά", + "2950": " aš", + "2951": " Der", + "2952": " коли", + "2953": " Γ", + "2954": "бва", + "2955": "ód", + "2956": "ικά", + "2957": " Bo", + "2958": " pet", + "2959": "ep", + "2960": "ποι", + "2961": "cut", + "2962": "ič", + "2963": "łem", + "2964": " kans", + "2965": "imas", + "2966": " само", + "2967": "bal", + "2968": "uda", + "2969": " ret", + "2970": " Nem", + "2971": "oss", + "2972": " när", + "2973": "ρή", + "2974": " tad", + "2975": " ovat", + "2976": " αλλά", + "2977": "ște", + "2978": "kar", + "2979": " niin", + "2980": " viņš", + "2981": "ljen", + "2982": " κάν", + "2983": " trans", + "2984": "ību", + "2985": " vain", + "2986": " Это", + "2987": " pří", + "2988": "ču", + "2989": " kul", + "2990": "ques", + "2991": " zas", + "2992": " они", + "2993": "atud", + "2994": "fici", + "2995": "ikt", + "2996": " його", + "2997": " video", + "2998": " disse", + "2999": " ins", + "3000": " Pa", + "3001": "ea", + "3002": "ūt", + "3003": " film", + "3004": "uw", + "3005": " via", + "3006": "änd", + "3007": " Ge", + "3008": "ines", + "3009": "рябва", + "3010": "anda", + "3011": "tal", + "3012": "agi", + "3013": " вони", + "3014": "chod", + "3015": " kaž", + "3016": " tego", + "3017": "клю", + "3018": " pan", + "3019": "rd", + "3020": "mal", + "3021": "amis", + "3022": " kli", + "3023": " muito", + "3024": " set", + "3025": " εξ", + "3026": "ции", + "3027": "вър", + "3028": " från", + "3029": "nym", + "3030": "kaz", + "3031": "nog", + "3032": "bra", + "3033": "íte", + "3034": "ке", + "3035": " jie", + "3036": " lig", + "3037": "ъде", + "3038": "ump", + "3039": " beh", + "3040": "ēja", + "3041": " dwar", + "3042": " jeho", + "3043": "pra", + "3044": " quel", + "3045": " три", + "3046": " fun", + "3047": "itate", + "3048": "emente", + "3049": "zzjoni", + "3050": "rei", + "3051": " ζ", + "3052": "lut", + "3053": "oto", + "3054": "har", + "3055": " Και", + "3056": "ře", + "3057": "ред", + "3058": " Sa", + "3059": " köz", + "3060": " või", + "3061": " które", + "3062": "вали", + "3063": " super", + "3064": " ļ", + "3065": "ża", + "3066": " Por", + "3067": "uti", + "3068": " доб", + "3069": "ções", + "3070": " Так", + "3071": "ży", + "3072": "inte", + "3073": " ні", + "3074": "able", + "3075": "reg", + "3076": " luc", + "3077": "ικό", + "3078": " At", + "3079": " á", + "3080": " deze", + "3081": "tid", + "3082": " turi", + "3083": "urde", + "3084": " оста", + "3085": " neb", + "3086": "nic", + "3087": " Dar", + "3088": " sist", + "3089": "nim", + "3090": " nel", + "3091": "car", + "3092": " euro", + "3093": "yan", + "3094": " aff", + "3095": "urt", + "3096": "три", + "3097": " vast", + "3098": "gr", + "3099": "ivo", + "3100": " blev", + "3101": "AA", + "3102": " які", + "3103": "čiau", + "3104": " Min", + "3105": "amp", + "3106": " което", + "3107": " zm", + "3108": " For", + "3109": " from", + "3110": " most", + "3111": " mir", + "3112": "που", + "3113": " amely", + "3114": "она", + "3115": " mô", + "3116": "ische", + "3117": " einen", + "3118": "sim", + "3119": " tym", + "3120": " Tas", + "3121": " nim", + "3122": " два", + "3123": " aber", + "3124": "мер", + "3125": " comme", + "3126": " zem", + "3127": "xt", + "3128": "lie", + "3129": "iki", + "3130": "ар", + "3131": "íme", + "3132": " tien", + "3133": "enden", + "3134": "fel", + "3135": "yen", + "3136": " inform", + "3137": " rend", + "3138": "ċi", + "3139": "dan", + "3140": " чу", + "3141": " cil", + "3142": "cket", + "3143": " util", + "3144": "aga", + "3145": " Per", + "3146": "ασ", + "3147": " sä", + "3148": "ция", + "3149": " Ž", + "3150": " zab", + "3151": " cos", + "3152": "żda", + "3153": " Ми", + "3154": " jew", + "3155": " tout", + "3156": " vēl", + "3157": "idade", + "3158": "zo", + "3159": " strat", + "3160": " dir", + "3161": " bila", + "3162": "itar", + "3163": "eket", + "3164": "ello", + "3165": "port", + "3166": "uten", + "3167": "δε", + "3168": " Su", + "3169": "uns", + "3170": "eft", + "3171": " fog", + "3172": " Ah", + "3173": " voi", + "3174": "hy", + "3175": "fen", + "3176": "śli", + "3177": " zak", + "3178": "iel", + "3179": "єте", + "3180": "erne", + "3181": " слу", + "3182": " kuin", + "3183": "iny", + "3184": " være", + "3185": "πω", + "3186": "ants", + "3187": " того", + "3188": "ього", + "3189": " nach", + "3190": " mutta", + "3191": "amb", + "3192": " oder", + "3193": " пер", + "3194": " Non", + "3195": "æn", + "3196": "itel", + "3197": " slu", + "3198": " Те", + "3199": "ellen", + "3200": "omen", + "3201": " svoj", + "3202": "ajú", + "3203": "вле", + "3204": " auch", + "3205": "umi", + "3206": "għu", + "3207": "out", + "3208": "chi", + "3209": " wird", + "3210": " ktoré", + "3211": " Das", + "3212": " jej", + "3213": " Amer", + "3214": "śmy", + "3215": "jek", + "3216": "klad", + "3217": " koh", + "3218": "pä", + "3219": "lag", + "3220": " Hu", + "3221": "utt", + "3222": "vent", + "3223": "ēm", + "3224": " loc", + "3225": " zin", + "3226": " але", + "3227": "tää", + "3228": " kaik", + "3229": " ima", + "3230": "over", + "3231": "μη", + "3232": "stru", + "3233": "vien", + "3234": "amas", + "3235": "isse", + "3236": "amen", + "3237": "ivat", + "3238": "áci", + "3239": "žen", + "3240": " acc", + "3241": "ode", + "3242": " entre", + "3243": " ville", + "3244": " sar", + "3245": "τική", + "3246": "ester", + "3247": " You", + "3248": " hade", + "3249": "taj", + "3250": " sti", + "3251": "tit", + "3252": " ties", + "3253": " Tr", + "3254": " tiegħu", + "3255": "nem", + "3256": "тому", + "3257": "obb", + "3258": " taf", + "3259": "vá", + "3260": " Ka", + "3261": "abil", + "3262": " kao", + "3263": " mens", + "3264": " Mis", + "3265": "iver", + "3266": " Dan", + "3267": "oga", + "3268": "ví", + "3269": "вать", + "3270": "hol", + "3271": " cli", + "3272": "han", + "3273": " pris", + "3274": "cher", + "3275": "кі", + "3276": "okat", + "3277": "jav", + "3278": "ted", + "3279": "cc", + "3280": " Ф", + "3281": "str", + "3282": " publi", + "3283": " nom", + "3284": "ną", + "3285": " our", + "3286": " skal", + "3287": "ové", + "3288": "ske", + "3289": "för", + "3290": "etti", + "3291": " vie", + "3292": " št", + "3293": " Con", + "3294": " lav", + "3295": "sak", + "3296": "kur", + "3297": " prob", + "3298": "ork", + "3299": "öt", + "3300": " keď", + "3301": " viņa", + "3302": "έρ", + "3303": " redu", + "3304": "odo", + "3305": "fr", + "3306": " sho", + "3307": " sve", + "3308": "chten", + "3309": " tror", + "3310": " arb", + "3311": " žel", + "3312": " dro", + "3313": "ματα", + "3314": " трябва", + "3315": "ves", + "3316": " tiet", + "3317": " Това", + "3318": "abb", + "3319": "ник", + "3320": " tiek", + "3321": "νω", + "3322": " bus", + "3323": " main", + "3324": " зро", + "3325": "inter", + "3326": "adas", + "3327": "ită", + "3328": "rai", + "3329": "ital", + "3330": " jsou", + "3331": "ób", + "3332": " få", + "3333": " вси", + "3334": "ця", + "3335": "amme", + "3336": " без", + "3337": " kir", + "3338": " jums", + "3339": "rž", + "3340": "хи", + "3341": " Ro", + "3342": " svoje", + "3343": "īg", + "3344": " pil", + "3345": " mie", + "3346": " ras", + "3347": "lin", + "3348": " тому", + "3349": " Ei", + "3350": " kann", + "3351": "īgi", + "3352": " foi", + "3353": " pow", + "3354": "éd", + "3355": "rze", + "3356": " laik", + "3357": " there", + "3358": " lie", + "3359": "amat", + "3360": " todos", + "3361": " ul", + "3362": " dou", + "3363": " Ku", + "3364": " informa", + "3365": "ked", + "3366": "ári", + "3367": "ētu", + "3368": " mint", + "3369": "λι", + "3370": " Les", + "3371": "kus", + "3372": "azione", + "3373": " prí", + "3374": " été", + "3375": " пот", + "3376": "лько", + "3377": " tudi", + "3378": " läh", + "3379": "olu", + "3380": "elijk", + "3381": " dod", + "3382": " mö", + "3383": "τητα", + "3384": " Ter", + "3385": " bħal", + "3386": " ση", + "3387": "ných", + "3388": "će", + "3389": " lan", + "3390": "cem", + "3391": " dla", + "3392": " pero", + "3393": "gar", + "3394": "chte", + "3395": "ents", + "3396": " vores", + "3397": "illä", + "3398": "ogra", + "3399": " které", + "3400": "enie", + "3401": " lin", + "3402": " kind", + "3403": "lament", + "3404": " когда", + "3405": "oup", + "3406": " dot", + "3407": " asta", + "3408": " X", + "3409": " fait", + "3410": " need", + "3411": " напра", + "3412": "ovať", + "3413": " Har", + "3414": " mid", + "3415": " fat", + "3416": " mel", + "3417": "iji", + "3418": " veel", + "3419": "ġa", + "3420": " sai", + "3421": "zie", + "3422": "ciones", + "3423": "kār", + "3424": " hen", + "3425": "tura", + "3426": " drž", + "3427": " Pri", + "3428": " nou", + "3429": " ner", + "3430": "ris", + "3431": " azt", + "3432": " wenn", + "3433": "ром", + "3434": "μό", + "3435": " Ele", + "3436": " сказа", + "3437": "рт", + "3438": " aktar", + "3439": " Em", + "3440": " rez", + "3441": " cum", + "3442": "edi", + "3443": "τού", + "3444": "тися", + "3445": " bude", + "3446": "heid", + "3447": " Ak", + "3448": " tol", + "3449": "ан", + "3450": " bek", + "3451": "δα", + "3452": "juk", + "3453": "nia", + "3454": " bara", + "3455": "ому", + "3456": " mult", + "3457": "ellä", + "3458": "ribu", + "3459": "ister", + "3460": " Wir", + "3461": "så", + "3462": " alebo", + "3463": "mat", + "3464": "idän", + "3465": " gli", + "3466": "kst", + "3467": "else", + "3468": "vers", + "3469": " prod", + "3470": " what", + "3471": " Εί", + "3472": " jih", + "3473": "ction", + "3474": " princip", + "3475": " ao", + "3476": " conc", + "3477": "ában", + "3478": "lem", + "3479": " боль", + "3480": " real", + "3481": "τυ", + "3482": "ным", + "3483": " John", + "3484": " meget", + "3485": " ili", + "3486": " more", + "3487": " kif", + "3488": " sit", + "3489": "antes", + "3490": " vara", + "3491": " lil", + "3492": " bardz", + "3493": "ejn", + "3494": " get", + "3495": " nebo", + "3496": " jā", + "3497": " Pre", + "3498": " Ab", + "3499": " zb", + "3500": "ujem", + "3501": "imą", + "3502": "jan", + "3503": " bis", + "3504": "вет", + "3505": " teh", + "3506": " ning", + "3507": " Ko", + "3508": " if", + "3509": " tagħna", + "3510": " syst", + "3511": "għa", + "3512": "tigt", + "3513": " aiz", + "3514": "kor", + "3515": " över", + "3516": "сля", + "3517": "szyst", + "3518": "SS", + "3519": "γα", + "3520": "kaan", + "3521": "elli", + "3522": " гра", + "3523": "jar", + "3524": " seu", + "3525": "ρισ", + "3526": " segu", + "3527": " hans", + "3528": "κο", + "3529": "iju", + "3530": " tied", + "3531": "ję", + "3532": "jas", + "3533": " Mi", + "3534": " тя", + "3535": " Ad", + "3536": " hod", + "3537": "μβ", + "3538": " θέ", + "3539": "hren", + "3540": " nez", + "3541": "рен", + "3542": " пи", + "3543": " zam", + "3544": " resp", + "3545": " ban", + "3546": " наши", + "3547": "hts", + "3548": "nou", + "3549": "žit", + "3550": " bru", + "3551": " Car", + "3552": " Ind", + "3553": " proces", + "3554": "ța", + "3555": "uo", + "3556": "фи", + "3557": "pat", + "3558": " Mais", + "3559": " všet", + "3560": "ksi", + "3561": "tern", + "3562": " män", + "3563": "ρέπει", + "3564": " вас", + "3565": "cti", + "3566": "zem", + "3567": " exist", + "3568": " kil", + "3569": "ake", + "3570": "iro", + "3571": "tain", + "3572": "jt", + "3573": "adi", + "3574": "фор", + "3575": "ling", + "3576": " gos", + "3577": " heeft", + "3578": " nuest", + "3579": "šč", + "3580": "aut", + "3581": " Το", + "3582": " plat", + "3583": " mijn", + "3584": " også", + "3585": " před", + "3586": "kli", + "3587": " rig", + "3588": " bre", + "3589": " ус", + "3590": "ző", + "3591": "ign", + "3592": " But", + "3593": " Ent", + "3594": " mí", + "3595": " ві", + "3596": "itas", + "3597": " hyv", + "3598": " vre", + "3599": " άλλ", + "3600": " essere", + "3601": "jal", + "3602": "υτό", + "3603": " forma", + "3604": "ogħ", + "3605": "mä", + "3606": "σουμε", + "3607": " saj", + "3608": " Ex", + "3609": " hon", + "3610": " jer", + "3611": " když", + "3612": " kül", + "3613": "зво", + "3614": "izi", + "3615": " test", + "3616": " Do", + "3617": " spre", + "3618": "chu", + "3619": "ємо", + "3620": "illi", + "3621": " moment", + "3622": " üt", + "3623": " port", + "3624": "pēc", + "3625": " επί", + "3626": " doll", + "3627": "asz", + "3628": "nego", + "3629": " your", + "3630": " biz", + "3631": " hem", + "3632": " aș", + "3633": " полу", + "3634": " fort", + "3635": "нии", + "3636": "lijk", + "3637": " stat", + "3638": "oso", + "3639": " face", + "3640": " біль", + "3641": " jekk", + "3642": " grande", + "3643": "ph", + "3644": " porque", + "3645": "ħa", + "3646": "lep", + "3647": " bę", + "3648": " Dob", + "3649": "öra", + "3650": " land", + "3651": "ilt", + "3652": "ėti", + "3653": "мы", + "3654": " mums", + "3655": " njeg", + "3656": " biti", + "3657": "vé", + "3658": " Od", + "3659": "biamo", + "3660": " anche", + "3661": "rot", + "3662": "рез", + "3663": " ó", + "3664": "ajā", + "3665": " tw", + "3666": "egen", + "3667": "isia", + "3668": " Ste", + "3669": "θεί", + "3670": "бле", + "3671": " czy", + "3672": " ec", + "3673": " кой", + "3674": "ikor", + "3675": " ani", + "3676": " дол", + "3677": "add", + "3678": " când", + "3679": "ūr", + "3680": " κατα", + "3681": " worden", + "3682": "lam", + "3683": " vas", + "3684": " прави", + "3685": " stra", + "3686": "jonijiet", + "3687": " És", + "3688": "itiv", + "3689": "szer", + "3690": "lov", + "3691": " tema", + "3692": " cette", + "3693": " eks", + "3694": " tor", + "3695": "voj", + "3696": " vě", + "3697": " bem", + "3698": "ск", + "3699": " compl", + "3700": "mm", + "3701": "prav", + "3702": " fer", + "3703": " hac", + "3704": " sat", + "3705": "тся", + "3706": " Ка", + "3707": " jin", + "3708": " zad", + "3709": "jor", + "3710": " Eu", + "3711": " sy", + "3712": "ener", + "3713": "jak", + "3714": "rib", + "3715": " gdy", + "3716": "zet", + "3717": "vin", + "3718": " ők", + "3719": "фе", + "3720": "ight", + "3721": "öd", + "3722": " mycket", + "3723": "ază", + "3724": " ø", + "3725": "ple", + "3726": "тра", + "3727": " fir", + "3728": "чески", + "3729": " lug", + "3730": "nan", + "3731": "éri", + "3732": "etta", + "3733": " zar", + "3734": "бли", + "3735": " ээ", + "3736": " jär", + "3737": " pare", + "3738": " Lu", + "3739": "ká", + "3740": "eden", + "3741": " bor", + "3742": " был", + "3743": " eles", + "3744": "ύτε", + "3745": "дь", + "3746": " ħa", + "3747": " dav", + "3748": " dacă", + "3749": " Au", + "3750": " natur", + "3751": "lt", + "3752": " Ac", + "3753": " molto", + "3754": " Br", + "3755": " glob", + "3756": " stand", + "3757": "ετε", + "3758": "τσι", + "3759": " απ", + "3760": " kw", + "3761": "kej", + "3762": " bal", + "3763": "urile", + "3764": "nis", + "3765": " kak", + "3766": " mad", + "3767": " nak", + "3768": " viet", + "3769": " mă", + "3770": " lang", + "3771": "iesz", + "3772": " nis", + "3773": "óó", + "3774": " gad", + "3775": " daudz", + "3776": " pros", + "3777": " toho", + "3778": "του", + "3779": " zg", + "3780": " oper", + "3781": " olen", + "3782": ",-", + "3783": "lav", + "3784": " zat", + "3785": " põ", + "3786": "ъм", + "3787": " ľ", + "3788": "ħseb", + "3789": "šen", + "3790": "ких", + "3791": " στα", + "3792": " best", + "3793": " fant", + "3794": " tempo", + "3795": "ġi", + "3796": " Мо", + "3797": " hor", + "3798": " était", + "3799": " timp", + "3800": "eder", + "3801": "ål", + "3802": " stan", + "3803": " andere", + "3804": " tamb", + "3805": " zw", + "3806": " meta", + "3807": " nije", + "3808": "ség", + "3809": "kä", + "3810": "νό", + "3811": "доб", + "3812": "съ", + "3813": " È", + "3814": "ды", + "3815": "šie", + "3816": " Але", + "3817": " myös", + "3818": " προσ", + "3819": " ord", + "3820": " skup", + "3821": "air", + "3822": "bién", + "3823": " след", + "3824": "ogu", + "3825": " pret", + "3826": " acest", + "3827": " той", + "3828": "čo", + "3829": " Med", + "3830": "urn", + "3831": " Ni", + "3832": "ence", + "3833": "ster", + "3834": " kienet", + "3835": "laus", + "3836": " jsme", + "3837": " mano", + "3838": "kan", + "3839": "kem", + "3840": " Á", + "3841": " hum", + "3842": " olla", + "3843": "ίο", + "3844": "ной", + "3845": "ком", + "3846": " viņi", + "3847": " Gr", + "3848": " kü", + "3849": " dien", + "3850": " осно", + "3851": " também", + "3852": " δύ", + "3853": " vše", + "3854": "вро", + "3855": " csak", + "3856": "sob", + "3857": " ļoti", + "3858": " Her", + "3859": " gå", + "3860": "тов", + "3861": "úú", + "3862": " Za", + "3863": " desc", + "3864": "amy", + "3865": "rob", + "3866": " där", + "3867": "niej", + "3868": " Hij", + "3869": " дуже", + "3870": "ender", + "3871": "aje", + "3872": " tij", + "3873": " време", + "3874": "elis", + "3875": " soci", + "3876": " sj", + "3877": " който", + "3878": "ké", + "3879": " Mor", + "3880": " știi", + "3881": " haar", + "3882": "col", + "3883": " apr", + "3884": "ondol", + "3885": "konom", + "3886": "бира", + "3887": "ningen", + "3888": "στη", + "3889": " ans", + "3890": "ulla", + "3891": "roj", + "3892": " μπορεί", + "3893": "tea", + "3894": " fakt", + "3895": "hor", + "3896": "azzjoni", + "3897": "ými", + "3898": " waren", + "3899": "ró", + "3900": " primer", + "3901": " gali", + "3902": " energ", + "3903": "μή", + "3904": " есть", + "3905": "раз", + "3906": " sõ", + "3907": " hur", + "3908": "īs", + "3909": "rag", + "3910": "üüd", + "3911": " ses", + "3912": " можете", + "3913": "ęs", + "3914": " Tā", + "3915": "μένο", + "3916": " ly", + "3917": "stav", + "3918": " sest", + "3919": " vál", + "3920": " Ob", + "3921": "usti", + "3922": "ycz", + "3923": "αααα", + "3924": " off", + "3925": " loro", + "3926": " πρό", + "3927": "anud", + "3928": "iera", + "3929": " proto", + "3930": "ham", + "3931": "ley", + "3932": " prá", + "3933": " about", + "3934": " questi", + "3935": "ekl", + "3936": " vam", + "3937": "pport", + "3938": "νή", + "3939": " ber", + "3940": " apie", + "3941": " moh", + "3942": "ņem", + "3943": "мя", + "3944": " няко", + "3945": " term", + "3946": " пі", + "3947": " cal", + "3948": " signi", + "3949": "pan", + "3950": "вся", + "3951": "нима", + "3952": " было", + "3953": "odi", + "3954": "ného", + "3955": " todo", + "3956": "jel", + "3957": " hä", + "3958": "δια", + "3959": "eng", + "3960": "стро", + "3961": "iß", + "3962": "ώρα", + "3963": " osob", + "3964": "ást", + "3965": "vy", + "3966": " inim", + "3967": " meie", + "3968": "vas", + "3969": "ħra", + "3970": " Δεν", + "3971": " також", + "3972": " wszyst", + "3973": " kle", + "3974": " Maar", + "3975": "ischen", + "3976": "ück", + "3977": " говори", + "3978": "ail", + "3979": "cije", + "3980": "ката", + "3981": "isesti", + "3982": "ød", + "3983": "atie", + "3984": " olyan", + "3985": "ρία", + "3986": "šanas", + "3987": "кт", + "3988": " verd", + "3989": "bes", + "3990": "ött", + "3991": " fal", + "3992": " ça", + "3993": " famil", + "3994": " poved", + "3995": " Sim", + "3996": " га", + "3997": "udi", + "3998": "γκ", + "3999": " Bar", + "4000": "iksi", + "4001": "oug", + "4002": "пол", + "4003": " Col", + "4004": " który", + "4005": " lõ", + "4006": "otre", + "4007": " pode", + "4008": "ára", + "4009": " Bu", + "4010": " Jis", + "4011": " koje", + "4012": " faire", + "4013": "гля", + "4014": "вання", + "4015": "ced", + "4016": " grand", + "4017": "hoz", + "4018": "жли", + "4019": " oč", + "4020": " Was", + "4021": "oon", + "4022": "ört", + "4023": " eso", + "4024": " conf", + "4025": " Ви", + "4026": " pove", + "4027": "ario", + "4028": " nagu", + "4029": " cilvē", + "4030": " hj", + "4031": "lic", + "4032": "чі", + "4033": " ці", + "4034": "inden", + "4035": "jd", + "4036": " sh", + "4037": " igen", + "4038": "τω", + "4039": "щото", + "4040": " tech", + "4041": " eh", + "4042": " Pero", + "4043": " tav", + "4044": " hvor", + "4045": "gg", + "4046": " také", + "4047": " abs", + "4048": " которые", + "4049": "žu", + "4050": "zę", + "4051": "лся", + "4052": " kop", + "4053": " können", + "4054": " miss", + "4055": "шу", + "4056": "jekt", + "4057": " παρα", + "4058": " було", + "4059": "itos", + "4060": "ále", + "4061": "abe", + "4062": " пу", + "4063": "ré", + "4064": "dzi", + "4065": " καθ", + "4066": " други", + "4067": " dak", + "4068": " nur", + "4069": " Lo", + "4070": " ye", + "4071": " jien", + "4072": " ř", + "4073": "ady", + "4074": " prim", + "4075": "iske", + "4076": "egu", + "4077": "рів", + "4078": " než", + "4079": " pier", + "4080": " vair", + "4081": "έν", + "4082": " Он", + "4083": "ieni", + "4084": " rest", + "4085": "dom", + "4086": " чо", + "4087": "ști", + "4088": "jiet", + "4089": " ang", + "4090": "змо", + "4091": " Aber", + "4092": " когато", + "4093": "of", + "4094": "kat", + "4095": " або", + "4096": "dje", + "4097": " él", + "4098": "ên", + "4099": "дей", + "4100": " mů", + "4101": " пе", + "4102": " preto", + "4103": " mno", + "4104": "bie", + "4105": "žete", + "4106": " έχει", + "4107": "kti", + "4108": "kså", + "4109": "stä", + "4110": " compr", + "4111": "кри", + "4112": "act", + "4113": " ref", + "4114": " був", + "4115": " habe", + "4116": " Він", + "4117": " wurde", + "4118": " myslím", + "4119": "ares", + "4120": " också", + "4121": "aties", + "4122": " byl", + "4123": " pit", + "4124": " delle", + "4125": "ube", + "4126": "otta", + "4127": " ple", + "4128": " word", + "4129": " вам", + "4130": " toim", + "4131": "ția", + "4132": " mūsu", + "4133": "uno", + "4134": " foarte", + "4135": " Θ", + "4136": " bare", + "4137": " знаете", + "4138": " ру", + "4139": " são", + "4140": " svar", + "4141": "ford", + "4142": "yw", + "4143": "æl", + "4144": "ody", + "4145": " être", + "4146": "øre", + "4147": "ures", + "4148": "jų", + "4149": " bio", + "4150": " τε", + "4151": " spo", + "4152": " rá", + "4153": "cas", + "4154": " denne", + "4155": " weet", + "4156": "dat", + "4157": "tes", + "4158": "usa", + "4159": " înt", + "4160": "ούν", + "4161": " mere", + "4162": " muut", + "4163": "arg", + "4164": " ευ", + "4165": " now", + "4166": " labai", + "4167": "öv", + "4168": "iner", + "4169": "jev", + "4170": " einem", + "4171": "ībā", + "4172": " vă", + "4173": " pel", + "4174": "gra", + "4175": "roz", + "4176": " rów", + "4177": " twe", + "4178": "čas", + "4179": " фи", + "4180": " zaj", + "4181": "liga", + "4182": "дите", + "4183": " ham", + "4184": "maz", + "4185": "nolog", + "4186": "ivel", + "4187": "oko", + "4188": "πάρχ", + "4189": " bin", + "4190": " Gre", + "4191": " prac", + "4192": "ovali", + "4193": "urg", + "4194": " Sh", + "4195": "chter", + "4196": "ním", + "4197": "stu", + "4198": "ров", + "4199": " Во", + "4200": " dobro", + "4201": " onder", + "4202": " organiz", + "4203": "zna", + "4204": " Quindi", + "4205": "sit", + "4206": " vse", + "4207": "ace", + "4208": " syn", + "4209": " szám", + "4210": " tej", + "4211": " casa", + "4212": "oved", + "4213": " minden", + "4214": "ay", + "4215": "atu", + "4216": " rob", + "4217": " deux", + "4218": "rom", + "4219": " sap", + "4220": "ój", + "4221": "бі", + "4222": " pus", + "4223": " would", + "4224": "сті", + "4225": " algo", + "4226": "lap", + "4227": " før", + "4228": " ges", + "4229": " dette", + "4230": "ppen", + "4231": "iausi", + "4232": " cri", + "4233": "riv", + "4234": " този", + "4235": " їх", + "4236": " vaik", + "4237": " cela", + "4238": "pla", + "4239": " cred", + "4240": "ху", + "4241": "essä", + "4242": "intre", + "4243": " onze", + "4244": "osto", + "4245": " fig", + "4246": " Nous", + "4247": "γά", + "4248": " думаю", + "4249": "nas", + "4250": " were", + "4251": "ică", + "4252": "ssä", + "4253": " Ό", + "4254": "idi", + "4255": "lura", + "4256": "σουν", + "4257": " drug", + "4258": "ree", + "4259": " οπο", + "4260": "том", + "4261": "εται", + "4262": " stato", + "4263": " gondol", + "4264": "cji", + "4265": " two", + "4266": "ші", + "4267": " sto", + "4268": " roku", + "4269": "din", + "4270": "rer", + "4271": "ivät", + "4272": "atta", + "4273": "pis", + "4274": " Fl", + "4275": " име", + "4276": " μπορού", + "4277": " deres", + "4278": " dell", + "4279": " kunnen", + "4280": "igi", + "4281": "μαι", + "4282": "ällä", + "4283": " njih", + "4284": "дела", + "4285": " Go", + "4286": "oria", + "4287": " fut", + "4288": "abar", + "4289": " visi", + "4290": "ъъ", + "4291": "ota", + "4292": "țin", + "4293": " Di", + "4294": " vill", + "4295": " tagħhom", + "4296": "ļu", + "4297": " ftit", + "4298": " Mutta", + "4299": "ności", + "4300": "ijos", + "4301": " pesso", + "4302": "īv", + "4303": " lát", + "4304": " diese", + "4305": "öl", + "4306": "ží", + "4307": "стве", + "4308": " odp", + "4309": " pole", + "4310": "φα", + "4311": "enia", + "4312": " Ein", + "4313": " hänen", + "4314": "akse", + "4315": "vän", + "4316": " áll", + "4317": "ême", + "4318": "enas", + "4319": "bin", + "4320": " Os", + "4321": " think", + "4322": " nod", + "4323": "ary", + "4324": "sel", + "4325": "ions", + "4326": " dėl", + "4327": "bat", + "4328": "cze", + "4329": " dva", + "4330": " suur", + "4331": " oss", + "4332": " prv", + "4333": " which", + "4334": " realmente", + "4335": "nú", + "4336": "buie", + "4337": " exper", + "4338": " despre", + "4339": "ξη", + "4340": " kit", + "4341": "oit", + "4342": "ws", + "4343": "ăt", + "4344": "λό", + "4345": " mūsų", + "4346": " Taigi", + "4347": " lim", + "4348": " san", + "4349": " treb", + "4350": "тер", + "4351": "ric", + "4352": " Ма", + "4353": "hel", + "4354": "ība", + "4355": " bý", + "4356": " Als", + "4357": " esto", + "4358": "pas", + "4359": "vait", + "4360": " gew", + "4361": "astă", + "4362": " Ω", + "4363": " uv", + "4364": " vám", + "4365": " tiegħi", + "4366": "ron", + "4367": " иска", + "4368": "áb", + "4369": " их", + "4370": "gram", + "4371": " naud", + "4372": " speci", + "4373": " zav", + "4374": " tren", + "4375": "тель", + "4376": "iser", + "4377": " minut", + "4378": "κι", + "4379": " einer", + "4380": " kõik", + "4381": " ци", + "4382": " daž", + "4383": " съм", + "4384": " him", + "4385": " memb", + "4386": "зы", + "4387": "cin", + "4388": "esen", + "4389": "nega", + "4390": "oten", + "4391": " cosa", + "4392": "δώ", + "4393": " κατά", + "4394": "kkor", + "4395": " lehet", + "4396": " their", + "4397": "itor", + "4398": " havde", + "4399": " pir", + "4400": " αυ", + "4401": "стра", + "4402": " bine", + "4403": " Ke", + "4404": " Som", + "4405": " sano", + "4406": "lia", + "4407": " time", + "4408": "esc", + "4409": " Tom", + "4410": " käyt", + "4411": "гла", + "4412": " jau", + "4413": " vás", + "4414": "dar", + "4415": " bene", + "4416": "fs", + "4417": "κολ", + "4418": " väga", + "4419": "hem", + "4420": " nego", + "4421": " them", + "4422": " tule", + "4423": "zik", + "4424": "eter", + "4425": "zin", + "4426": " chc", + "4427": " palju", + "4428": "nosť", + "4429": "aba", + "4430": "ird", + "4431": "nad", + "4432": "cej", + "4433": " пове", + "4434": "egi", + "4435": " ω", + "4436": "som", + "4437": " ką", + "4438": " meer", + "4439": " ден", + "4440": "υσ", + "4441": "ské", + "4442": " сме", + "4443": "rok", + "4444": "ίνει", + "4445": " uno", + "4446": " wel", + "4447": " senti", + "4448": "qa", + "4449": " помо", + "4450": " jotka", + "4451": "īm", + "4452": "ool", + "4453": " ljudi", + "4454": " ér", + "4455": " lub", + "4456": " υπο", + "4457": " much", + "4458": "teri", + "4459": " aug", + "4460": " ces", + "4461": "ople", + "4462": "tam", + "4463": "line", + "4464": " bei", + "4465": " geen", + "4466": "дат", + "4467": "undo", + "4468": " naz", + "4469": "ючи", + "4470": " üks", + "4471": " ж", + "4472": "tere", + "4473": " ри", + "4474": "erna", + "4475": " sid", + "4476": " πρέπει", + "4477": "шен", + "4478": " эти", + "4479": "ring", + "4480": "стоя", + "4481": " koll", + "4482": " possi", + "4483": " przez", + "4484": " naše", + "4485": "isin", + "4486": " mån", + "4487": " vlast", + "4488": " amb", + "4489": " отно", + "4490": "fik", + "4491": " í", + "4492": " még", + "4493": "зможно", + "4494": "irt", + "4495": "сет", + "4496": " stru", + "4497": " tipo", + "4498": " esper", + "4499": " μό", + "4500": " zou", + "4501": " сте", + "4502": " Той", + "4503": " tit", + "4504": " πρα", + "4505": " тре", + "4506": " Mislim", + "4507": "lé", + "4508": "ök", + "4509": "ittä", + "4510": "ají", + "4511": " там", + "4512": " zal", + "4513": "imal", + "4514": " espe", + "4515": " iesp", + "4516": "ιδ", + "4517": "зе", + "4518": "pak", + "4519": "avez", + "4520": " defin", + "4521": "app", + "4522": " dog", + "4523": " Pe", + "4524": "owie", + "4525": " Mon", + "4526": " Kas", + "4527": " jel", + "4528": " към", + "4529": "лно", + "4530": " од", + "4531": " hvad", + "4532": "gov", + "4533": "isz", + "4534": "owe", + "4535": " люди", + "4536": "ствен", + "4537": " ment", + "4538": " precis", + "4539": "ño", + "4540": " savu", + "4541": " бъде", + "4542": " Sam", + "4543": " тези", + "4544": " selv", + "4545": "com", + "4546": " may", + "4547": "arna", + "4548": " úgy", + "4549": "ujú", + "4550": "ким", + "4551": "anden", + "4552": " Huwa", + "4553": " nagyon", + "4554": "æt", + "4555": " Kui", + "4556": " tista", + "4557": " Pol", + "4558": " Europe", + "4559": " вър", + "4560": " также", + "4561": "tag", + "4562": " muy", + "4563": "нг", + "4564": "isch", + "4565": "рата", + "4566": " some", + "4567": "vod", + "4568": "ibt", + "4569": "pus", + "4570": " сло", + "4571": " Pod", + "4572": "cional", + "4573": "λει", + "4574": " če", + "4575": "rast", + "4576": "чень", + "4577": " red", + "4578": " redz", + "4579": " ktorý", + "4580": " got", + "4581": " mitä", + "4582": "ms", + "4583": "pă", + "4584": "ijas", + "4585": " Então", + "4586": " comun", + "4587": "onna", + "4588": " ihr", + "4589": "прав", + "4590": "мент", + "4591": "ală", + "4592": "jeb", + "4593": " ils", + "4594": " який", + "4595": " Egy", + "4596": " tiegħek", + "4597": " meu", + "4598": "tev", + "4599": " Des", + "4600": "μπ", + "4601": " viel", + "4602": " cost", + "4603": " równie", + "4604": "rac", + "4605": "ків", + "4606": " viņu", + "4607": " kurie", + "4608": "lič", + "4609": " Mu", + "4610": " elő", + "4611": " when", + "4612": " пробле", + "4613": "стви", + "4614": " teraz", + "4615": " Ol", + "4616": "лы", + "4617": "lab", + "4618": "čių", + "4619": "press", + "4620": " fran", + "4621": " Щ", + "4622": " klas", + "4623": "нието", + "4624": " kons", + "4625": " aceste", + "4626": " result", + "4627": " kri", + "4628": " sok", + "4629": " dintre", + "4630": "ęp", + "4631": " který", + "4632": "ψη", + "4633": " kde", + "4634": " ain", + "4635": " zag", + "4636": "бно", + "4637": "lige", + "4638": " nei", + "4639": " noch", + "4640": " popul", + "4641": " Li", + "4642": "пита", + "4643": " Jed", + "4644": " très", + "4645": " годи", + "4646": " dest", + "4647": "rin", + "4648": " want", + "4649": "mun", + "4650": "avat", + "4651": " līdz", + "4652": " ember", + "4653": "hux", + "4654": " hacer", + "4655": " propri", + "4656": "qui", + "4657": " Om", + "4658": " Бо", + "4659": " ast", + "4660": " mislim", + "4661": "has", + "4662": "rea", + "4663": " lok", + "4664": " Pot", + "4665": " Los", + "4666": " Este", + "4667": " δε", + "4668": " difer", + "4669": " autom", + "4670": "ėt", + "4671": " cir", + "4672": " noget", + "4673": " ekonom", + "4674": "adz", + "4675": "yy", + "4676": "ciju", + "4677": " czas", + "4678": " prot", + "4679": " momento", + "4680": "лан", + "4681": " ră", + "4682": " τέ", + "4683": "рите", + "4684": "itte", + "4685": " каза", + "4686": " vär", + "4687": "бити", + "4688": "ones", + "4689": " Val", + "4690": "ieren", + "4691": "ják", + "4692": "ktor", + "4693": " doen", + "4694": " если", + "4695": " lucru", + "4696": "άρ", + "4697": "rač", + "4698": "ół", + "4699": " jas", + "4700": "ільки", + "4701": "xi", + "4702": "poi", + "4703": " буду", + "4704": " ако", + "4705": " transp", + "4706": "ції", + "4707": "atur", + "4708": " дей", + "4709": " doar", + "4710": " bardzo", + "4711": " perché", + "4712": "χα", + "4713": "aux", + "4714": " fot", + "4715": "ście", + "4716": " zur", + "4717": " мене", + "4718": " potreb", + "4719": " zich", + "4720": "igh", + "4721": " pró", + "4722": " York", + "4723": "iog", + "4724": " dus", + "4725": " liet", + "4726": " всички", + "4727": " nen", + "4728": " finns", + "4729": " jeden", + "4730": " mich", + "4731": " amikor", + "4732": "тно", + "4733": " Така", + "4734": " posto", + "4735": " również", + "4736": "pend", + "4737": " cuando", + "4738": " tir", + "4739": "gin", + "4740": " Ger", + "4741": "бра", + "4742": "jes", + "4743": " stad", + "4744": " sik", + "4745": "rzy", + "4746": " há", + "4747": " égal", + "4748": " pes", + "4749": " serv", + "4750": "uz", + "4751": "used", + "4752": " Kun", + "4753": " între", + "4754": "вто", + "4755": "ная", + "4756": "ować", + "4757": "ward", + "4758": " lis", + "4759": " lihts", + "4760": "δή", + "4761": " ami", + "4762": " tän", + "4763": " hely", + "4764": "ador", + "4765": "tais", + "4766": " ons", + "4767": " она", + "4768": "lasti", + "4769": " quer", + "4770": " fazer", + "4771": "хме", + "4772": "idades", + "4773": " hvis", + "4774": " într", + "4775": " Taip", + "4776": " inten", + "4777": "det", + "4778": "σία", + "4779": "aria", + "4780": " pirm", + "4781": " вона", + "4782": "mos", + "4783": " îm", + "4784": " Pok", + "4785": "ξει", + "4786": "ních", + "4787": " ble", + "4788": " ī", + "4789": " работа", + "4790": "кре", + "4791": " past", + "4792": "cause", + "4793": " Λ", + "4794": "ято", + "4795": "jali", + "4796": " Don", + "4797": " she", + "4798": "uer", + "4799": "ænd", + "4800": "тите", + "4801": "bbli", + "4802": " jon", + "4803": " τό", + "4804": "onder", + "4805": " vá", + "4806": " câ", + "4807": " през", + "4808": " notre", + "4809": "ait", + "4810": "ρου", + "4811": "дер", + "4812": "robi", + "4813": " investi", + "4814": "kim", + "4815": "вам", + "4816": "δη", + "4817": " ċ", + "4818": "ées", + "4819": "ээээ", + "4820": " actu", + "4821": " reik", + "4822": " hanno", + "4823": "abel", + "4824": "idos", + "4825": "quer", + "4826": " väh", + "4827": " обра", + "4828": " prote", + "4829": " également", + "4830": "πη", + "4831": " eng", + "4832": " göra", + "4833": " Viņš", + "4834": "нен", + "4835": " än", + "4836": " protože", + "4837": " mik", + "4838": "ily", + "4839": "estä", + "4840": " Mus", + "4841": " priv", + "4842": "ují", + "4843": "zel", + "4844": " prop", + "4845": "βα", + "4846": "ätkö", + "4847": " mensen", + "4848": "dem", + "4849": "νομ", + "4850": "ames", + "4851": " zosta", + "4852": "EEEEEEEE", + "4853": " durante", + "4854": "ao", + "4855": " вико", + "4856": " liber", + "4857": " domāju", + "4858": " Як", + "4859": "ges", + "4860": " αυτή", + "4861": "ása", + "4862": " ξέ", + "4863": " Rob", + "4864": " рас", + "4865": " меня", + "4866": "пі", + "4867": "nit", + "4868": "raj", + "4869": "рова", + "4870": " bro", + "4871": " oprav", + "4872": " До", + "4873": " Tam", + "4874": " ela", + "4875": " kaj", + "4876": " wordt", + "4877": "vr", + "4878": " tě", + "4879": " boli", + "4880": " eg", + "4881": "łu", + "4882": " її", + "4883": " очень", + "4884": "vari", + "4885": " pav", + "4886": " Ve", + "4887": " vaj", + "4888": " avons", + "4889": "etzt", + "4890": "idas", + "4891": " ты", + "4892": "pec", + "4893": " bon", + "4894": " jagħ", + "4895": " pretože", + "4896": " един", + "4897": "ίε", + "4898": " рі", + "4899": "imento", + "4900": "mă", + "4901": "tik", + "4902": "olla", + "4903": " Kat", + "4904": " kre", + "4905": " darb", + "4906": "cat", + "4907": " Οι", + "4908": " been", + "4909": "łe", + "4910": " Você", + "4911": " több", + "4912": " Про", + "4913": "anta", + "4914": "fatt", + "4915": " být", + "4916": "koz", + "4917": "ωωωω", + "4918": " Nä", + "4919": " cen", + "4920": "fica", + "4921": " тази", + "4922": "uses", + "4923": " ziniet", + "4924": "anza", + "4925": "aient", + "4926": "aren", + "4927": "elik", + "4928": " forts", + "4929": " Αυτό", + "4930": " nás", + "4931": " pää", + "4932": " akkor", + "4933": "što", + "4934": " sabe", + "4935": " pär", + "4936": " koska", + "4937": "ника", + "4938": " mehr", + "4939": " него", + "4940": "гле", + "4941": "ajte", + "4942": " rem", + "4943": " якщо", + "4944": " strateg", + "4945": "eksi", + "4946": " lik", + "4947": "voir", + "4948": " prima", + "4949": "fon", + "4950": " būti", + "4951": " even", + "4952": " život", + "4953": " maž", + "4954": " people", + "4955": "τρο", + "4956": " Reg", + "4957": " għa", + "4958": "öm", + "4959": " Chr", + "4960": " πραγμα", + "4961": " Kai", + "4962": " tú", + "4963": "aste", + "4964": " прои", + "4965": "kay", + "4966": " kod", + "4967": " lag", + "4968": " tega", + "4969": " nekaj", + "4970": " Jak", + "4971": " dzie", + "4972": " hekk", + "4973": " Ten", + "4974": " cit", + "4975": "ră", + "4976": "ēju", + "4977": " Va", + "4978": "šim", + "4979": " cur", + "4980": " who", + "4981": "āl", + "4982": " podob", + "4983": " Singapur", + "4984": " Singapore", + "4985": " sli", + "4986": " consider", + "4987": "ład", + "4988": " cui", + "4989": " pis", + "4990": " amer", + "4991": " essa", + "4992": " koja", + "4993": " werd", + "4994": "кой", + "4995": " mä", + "4996": " Ра", + "4997": " Europa", + "4998": " sud", + "4999": "λώ", + "5000": "σαν", + "5001": " гру", + "5002": " więc", + "5003": "kie", + "5004": "stvo", + "5005": " versch", + "5006": "itan", + "5007": " kienu", + "5008": "ними", + "5009": " която", + "5010": "mest", + "5011": "med", + "5012": " malo", + "5013": " υπάρχ", + "5014": " vraiment", + "5015": "alen", + "5016": " aux", + "5017": "kow", + "5018": " мне", + "5019": " importante", + "5020": "ικο", + "5021": " dif", + "5022": " сдела", + "5023": "ya", + "5024": "liv", + "5025": " alc", + "5026": " suo", + "5027": " funk", + "5028": " here", + "5029": " detta", + "5030": "rik", + "5031": "σετε", + "5032": "éc", + "5033": "όν", + "5034": "тели", + "5035": " fav", + "5036": "tige", + "5037": " jen", + "5038": " sek", + "5039": " dim", + "5040": " доста", + "5041": "juč", + "5042": " tiem", + "5043": "σσ", + "5044": " sent", + "5045": " visu", + "5046": " hv", + "5047": " cl", + "5048": "cord", + "5049": " oleks", + "5050": " már", + "5051": "γή", + "5052": "imme", + "5053": " această", + "5054": "ule", + "5055": "υνα", + "5056": " cele", + "5057": " apro", + "5058": " ¡", + "5059": "esa", + "5060": "ман", + "5061": "гі", + "5062": " disk", + "5063": " όταν", + "5064": " άν", + "5065": " suc", + "5066": " use", + "5067": " žm", + "5068": " jedn", + "5069": " nella", + "5070": "cil", + "5071": " -", + "5072": "καν", + "5073": "elor", + "5074": " exc", + "5075": " svet", + "5076": " simpl", + "5077": "χή", + "5078": " δύο", + "5079": " vendar", + "5080": "дов", + "5081": " δη", + "5082": " dzi", + "5083": " donc", + "5084": " kult", + "5085": "nut", + "5086": "áme", + "5087": " им", + "5088": " bħ", + "5089": " над", + "5090": "SA", + "5091": "πί", + "5092": "rim", + "5093": " arm", + "5094": " Как", + "5095": " vairāk", + "5096": " Ú", + "5097": "ots", + "5098": " φο", + "5099": "dział", + "5100": " kuul", + "5101": " kanssa", + "5102": "iert", + "5103": "τρα", + "5104": "minist", + "5105": "oper", + "5106": " ves", + "5107": " modo", + "5108": " tkun", + "5109": " пока", + "5110": "вор", + "5111": "ном", + "5112": "inge", + "5113": " ann", + "5114": " așa", + "5115": " nau", + "5116": " rezult", + "5117": " būt", + "5118": " lev", + "5119": "issen", + "5120": "apt", + "5121": " više", + "5122": "μι", + "5123": " быть", + "5124": "ók", + "5125": " iżda", + "5126": "że", + "5127": "īja", + "5128": " Αλλά", + "5129": "lub", + "5130": " ya", + "5131": " hos", + "5132": " них", + "5133": "istas", + "5134": "ót", + "5135": "fol", + "5136": " κο", + "5137": " ľud", + "5138": "ência", + "5139": " добре", + "5140": "uts", + "5141": " Jah", + "5142": " Ed", + "5143": " suis", + "5144": "čnosti", + "5145": "чай", + "5146": "uter", + "5147": " byť", + "5148": " Tämä", + "5149": " veľmi", + "5150": "ož", + "5151": " Kan", + "5152": " elle", + "5153": " siitä", + "5154": "циа", + "5155": "auc", + "5156": " toh", + "5157": " déc", + "5158": " sei", + "5159": " ħaġa", + "5160": " через", + "5161": "ism", + "5162": " ev", + "5163": " ži", + "5164": " kus", + "5165": "bagħ", + "5166": " vår", + "5167": " nich", + "5168": " econom", + "5169": "ates", + "5170": " doc", + "5171": " gör", + "5172": " tart", + "5173": " ust", + "5174": " vienkār", + "5175": " amit", + "5176": " будет", + "5177": " también", + "5178": "ror", + "5179": "рта", + "5180": "aa", + "5181": "кого", + "5182": " Tod", + "5183": " Tur", + "5184": " był", + "5185": " jäl", + "5186": " нам", + "5187": " вели", + "5188": "qs", + "5189": "ög", + "5190": " trebuie", + "5191": " tin", + "5192": " όπω", + "5193": "ering", + "5194": "рти", + "5195": " pam", + "5196": " στον", + "5197": "rab", + "5198": " šo", + "5199": " tat", + "5200": " gospod", + "5201": "стина", + "5202": " Ih", + "5203": " Não", + "5204": "φή", + "5205": "ách", + "5206": "жна", + "5207": " Dak", + "5208": " dre", + "5209": " mogu", + "5210": "άζ", + "5211": " rod", + "5212": " kollha", + "5213": "šn", + "5214": " vec", + "5215": " každ", + "5216": " dessa", + "5217": "uró", + "5218": "was", + "5219": "elij", + "5220": " bili", + "5221": " sehr", + "5222": "utat", + "5223": "lej", + "5224": " σύ", + "5225": " Jos", + "5226": " bit", + "5227": "ři", + "5228": "elles", + "5229": "pol", + "5230": " sna", + "5231": "ised", + "5232": " Vous", + "5233": " μετα", + "5234": " problema", + "5235": " andra", + "5236": " bun", + "5237": "stand", + "5238": " helt", + "5239": " constru", + "5240": " lihtsalt", + "5241": " log", + "5242": " frem", + "5243": "plo", + "5244": " hoe", + "5245": " into", + "5246": " Ü", + "5247": " ακ", + "5248": "kte", + "5249": " uk", + "5250": "jau", + "5251": " Ce", + "5252": " nyt", + "5253": " πιο", + "5254": " very", + "5255": " цього", + "5256": "ex", + "5257": " soll", + "5258": " going", + "5259": " sempli", + "5260": "ämän", + "5261": " lep", + "5262": " εδώ", + "5263": " välja", + "5264": "sin", + "5265": "тен", + "5266": " gam", + "5267": " Зна", + "5268": " prem", + "5269": " szeret", + "5270": "ést", + "5271": "над", + "5272": "umus", + "5273": " går", + "5274": " jog", + "5275": " ді", + "5276": " vys", + "5277": "ară", + "5278": "ños", + "5279": "jam", + "5280": "zni", + "5281": "inė", + "5282": " ing", + "5283": " Deci", + "5284": " peut", + "5285": " Ph", + "5286": "estu", + "5287": " moet", + "5288": "στα", + "5289": " Or", + "5290": " preg", + "5291": " Мы", + "5292": "obil", + "5293": "țion", + "5294": " запо", + "5295": "ній", + "5296": " My", + "5297": " prez", + "5298": " får", + "5299": " осо", + "5300": " poten", + "5301": "ħed", + "5302": " Се", + "5303": " mok", + "5304": "ivi", + "5305": "pie", + "5306": "iset", + "5307": " täh", + "5308": "зі", + "5309": " siin", + "5310": " один", + "5311": "ную", + "5312": "għid", + "5313": " Mēs", + "5314": " tikai", + "5315": " Ш", + "5316": "rau", + "5317": "зда", + "5318": "кто", + "5319": " Είναι", + "5320": "emi", + "5321": "erea", + "5322": " Kar", + "5323": " στι", + "5324": " вер", + "5325": " Å", + "5326": "äng", + "5327": "ųjų", + "5328": "mien", + "5329": " moje", + "5330": "тів", + "5331": " arv", + "5332": " unter", + "5333": " due", + "5334": " tel", + "5335": " mani", + "5336": "īgs", + "5337": "лаго", + "5338": " дела", + "5339": " tanto", + "5340": " otr", + "5341": " wil", + "5342": " interess", + "5343": " ehm", + "5344": "cker", + "5345": "ds", + "5346": "ful", + "5347": " deb", + "5348": "ungs", + "5349": " σημα", + "5350": "zza", + "5351": "γρα", + "5352": " μη", + "5353": "aszt", + "5354": "ossa", + "5355": " fond", + "5356": "pin", + "5357": " mare", + "5358": " måste", + "5359": "τώ", + "5360": " ги", + "5361": " Char", + "5362": "fü", + "5363": "už", + "5364": "ură", + "5365": "atti", + "5366": "jeme", + "5367": " nagħ", + "5368": " grund", + "5369": " dô", + "5370": " así", + "5371": " disc", + "5372": " компа", + "5373": "ían", + "5374": " hawn", + "5375": " durch", + "5376": " univers", + "5377": " ry", + "5378": " Ako", + "5379": " Bro", + "5380": " then", + "5381": " weiß", + "5382": "bagħad", + "5383": " fă", + "5384": " takže", + "5385": " politi", + "5386": " való", + "5387": "oare", + "5388": " estava", + "5389": "rak", + "5390": " Tu", + "5391": "żi", + "5392": "hmen", + "5393": " interes", + "5394": " ket", + "5395": " mell", + "5396": "ory", + "5397": "rum", + "5398": " minu", + "5399": " ef", + "5400": "fu", + "5401": "kla", + "5402": " ло", + "5403": " regul", + "5404": " its", + "5405": "øl", + "5406": "śl", + "5407": "ème", + "5408": " új", + "5409": "inių", + "5410": " powie", + "5411": " rag", + "5412": " хоро", + "5413": " paljon", + "5414": " tiesiog", + "5415": "heit", + "5416": " veliko", + "5417": "ape", + "5418": "hand", + "5419": " alles", + "5420": " людей", + "5421": " Mad", + "5422": " πρά", + "5423": " všech", + "5424": "mission", + "5425": "adu", + "5426": " Fil", + "5427": " mun", + "5428": " Mc", + "5429": "aff", + "5430": " rom", + "5431": "atori", + "5432": "λω", + "5433": "oden", + "5434": " Áno", + "5435": " noast", + "5436": "avi", + "5437": " kart", + "5438": "cias", + "5439": " bör", + "5440": " vri", + "5441": "ání", + "5442": "itud", + "5443": "onte", + "5444": "ében", + "5445": " ren", + "5446": " час", + "5447": " Miss", + "5448": " fram", + "5449": "duc", + "5450": " vak", + "5451": "iente", + "5452": " επίση", + "5453": "dę", + "5454": "дъл", + "5455": " trab", + "5456": "alu", + "5457": "тив", + "5458": " ří", + "5459": "ture", + "5460": " Tag", + "5461": "гна", + "5462": " prov", + "5463": " encont", + "5464": "óg", + "5465": "elő", + "5466": "lja", + "5467": "inta", + "5468": "ονται", + "5469": " gibt", + "5470": " sitä", + "5471": " fre", + "5472": "λύτε", + "5473": " sis", + "5474": " stvari", + "5475": "åt", + "5476": "ress", + "5477": "pper", + "5478": " Vai", + "5479": " spus", + "5480": " votre", + "5481": " kaikki", + "5482": "ню", + "5483": "šanu", + "5484": "šli", + "5485": " lugar", + "5486": " Cu", + "5487": "вропе", + "5488": " spor", + "5489": "ână", + "5490": " също", + "5491": "dev", + "5492": " ισ", + "5493": " būtu", + "5494": " così", + "5495": " pense", + "5496": " questa", + "5497": "ison", + "5498": "лага", + "5499": " rze", + "5500": " πό", + "5501": "ange", + "5502": "tima", + "5503": " vert", + "5504": "omis", + "5505": " πάν", + "5506": " cres", + "5507": "ště", + "5508": " ξε", + "5509": "zien", + "5510": " tyl", + "5511": " upor", + "5512": " indust", + "5513": "oh", + "5514": "nov", + "5515": "wij", + "5516": " были", + "5517": " kunne", + "5518": "лее", + "5519": " гла", + "5520": "wel", + "5521": "цен", + "5522": " át", + "5523": "cijas", + "5524": "нес", + "5525": "ką", + "5526": "rait", + "5527": "рно", + "5528": " neces", + "5529": " ped", + "5530": " dann", + "5531": " mert", + "5532": " vida", + "5533": " situa", + "5534": " sempre", + "5535": " abbiamo", + "5536": "сле", + "5537": "nies", + "5538": " być", + "5539": " jego", + "5540": " мені", + "5541": "aw", + "5542": " izm", + "5543": " gel", + "5544": "ņu", + "5545": "cir", + "5546": " Mal", + "5547": " ί", + "5548": "sid", + "5549": "чат", + "5550": " förs", + "5551": " absol", + "5552": " drugi", + "5553": " proti", + "5554": " Хо", + "5555": " багато", + "5556": "ča", + "5557": " ever", + "5558": " отри", + "5559": "tenant", + "5560": " hatte", + "5561": " hy", + "5562": " ihm", + "5563": " pia", + "5564": " всі", + "5565": " Jien", + "5566": "hos", + "5567": "atte", + "5568": " any", + "5569": " mhux", + "5570": "klich", + "5571": " eigen", + "5572": " Pu", + "5573": " vod", + "5574": " сві", + "5575": " trava", + "5576": "ują", + "5577": "inst", + "5578": " trabal", + "5579": "das", + "5580": " sia", + "5581": "way", + "5582": "лови", + "5583": "ování", + "5584": " last", + "5585": " бути", + "5586": "мента", + "5587": "itatea", + "5588": " persone", + "5589": "opa", + "5590": " Kon", + "5591": "enter", + "5592": "коно", + "5593": " leur", + "5594": " nech", + "5595": "fri", + "5596": " бя", + "5597": " цен", + "5598": " bħala", + "5599": " savez", + "5600": " really", + "5601": "ix", + "5602": " ál", + "5603": "yje", + "5604": "jich", + "5605": "θηκε", + "5606": " jei", + "5607": " тех", + "5608": " Komis", + "5609": "ερ", + "5610": "etr", + "5611": " même", + "5612": "otu", + "5613": "жду", + "5614": " лі", + "5615": " Nad", + "5616": " вклю", + "5617": " konkur", + "5618": "пла", + "5619": "чение", + "5620": "ír", + "5621": "orit", + "5622": " pessoas", + "5623": "θή", + "5624": "anz", + "5625": "til", + "5626": "umas", + "5627": " merc", + "5628": " stvar", + "5629": " повече", + "5630": "λου", + "5631": "вав", + "5632": " испо", + "5633": "νά", + "5634": "bet", + "5635": " said", + "5636": "ľa", + "5637": "ným", + "5638": "oku", + "5639": " χα", + "5640": " sü", + "5641": "aron", + "5642": "ço", + "5643": " cul", + "5644": "umiem", + "5645": " chose", + "5646": " bylo", + "5647": "pid", + "5648": " té", + "5649": " biss", + "5650": " σκ", + "5651": " зо", + "5652": " imma", + "5653": " нещо", + "5654": " avait", + "5655": " предста", + "5656": "erson", + "5657": " stabil", + "5658": "dal", + "5659": "ative", + "5660": " uč", + "5661": " kesk", + "5662": "цю", + "5663": "üh", + "5664": " ért", + "5665": " cond", + "5666": " denk", + "5667": "lá", + "5668": " kis", + "5669": " swo", + "5670": " bolo", + "5671": " tead", + "5672": " moeten", + "5673": "ór", + "5674": "ppe", + "5675": " anti", + "5676": "jut", + "5677": " ее", + "5678": "σσότε", + "5679": " Geor", + "5680": " είχε", + "5681": " então", + "5682": " περισσότε", + "5683": "uli", + "5684": " Las", + "5685": " Nor", + "5686": "gende", + "5687": " spos", + "5688": "wk", + "5689": "fach", + "5690": "vero", + "5691": "nyt", + "5692": " tou", + "5693": " būtų", + "5694": "ća", + "5695": "umā", + "5696": "ált", + "5697": "inda", + "5698": " hele", + "5699": " Iz", + "5700": " защото", + "5701": " partner", + "5702": "yllä", + "5703": " dez", + "5704": " nuo", + "5705": " wür", + "5706": " sigur", + "5707": " Če", + "5708": "icht", + "5709": "писа", + "5710": " jist", + "5711": "ťa", + "5712": "ρια", + "5713": "лия", + "5714": " töö", + "5715": " asem", + "5716": " echt", + "5717": " exemp", + "5718": "inud", + "5719": "ravo", + "5720": " gla", + "5721": " vég", + "5722": "ned", + "5723": "amil", + "5724": " może", + "5725": " були", + "5726": " these", + "5727": "ďal", + "5728": "ieważ", + "5729": " base", + "5730": " fare", + "5731": " multe", + "5732": "гли", + "5733": " две", + "5734": " poss", + "5735": "rez", + "5736": "ших", + "5737": " Dav", + "5738": " kör", + "5739": " peu", + "5740": "nika", + "5741": " Iva", + "5742": " run", + "5743": " prime", + "5744": " vienkārši", + "5745": "ļa", + "5746": " Ä", + "5747": "aig", + "5748": " lé", + "5749": "ager", + "5750": "erste", + "5751": " había", + "5752": "cos", + "5753": " κάπο", + "5754": "šte", + "5755": "реди", + "5756": " hot", + "5757": "ступ", + "5758": " dia", + "5759": " fem", + "5760": "govor", + "5761": "zej", + "5762": "нето", + "5763": "icu", + "5764": "oth", + "5765": "keit", + "5766": " tis", + "5767": " San", + "5768": "iems", + "5769": " mam", + "5770": "anten", + "5771": " perm", + "5772": " intern", + "5773": "ugh", + "5774": "mlu", + "5775": "osten", + "5776": " zien", + "5777": "urd", + "5778": "enza", + "5779": "десет", + "5780": " goed", + "5781": " mand", + "5782": " vier", + "5783": " aktiv", + "5784": " время", + "5785": " vē", + "5786": " ха", + "5787": " iespēj", + "5788": " κοι", + "5789": " κάτι", + "5790": "far", + "5791": "від", + "5792": "вата", + "5793": " gaan", + "5794": " tämä", + "5795": "aki", + "5796": " év", + "5797": "plat", + "5798": "tica", + "5799": " tikr", + "5800": " ασ", + "5801": "était", + "5802": " tutto", + "5803": " invest", + "5804": "row", + "5805": "вропей", + "5806": " tento", + "5807": "th", + "5808": "egy", + "5809": "чина", + "5810": " led", + "5811": " pēc", + "5812": " något", + "5813": " usk", + "5814": "ħab", + "5815": "λευ", + "5816": " ting", + "5817": " toda", + "5818": " έχουν", + "5819": "μφ", + "5820": " jį", + "5821": " učin", + "5822": " zelo", + "5823": " потрі", + "5824": " problém", + "5825": " Ag", + "5826": " Aust", + "5827": " Έτσι", + "5828": "umo", + "5829": " krit", + "5830": " hol", + "5831": " This", + "5832": " však", + "5833": "alifor", + "5834": " ütles", + "5835": "āv", + "5836": "ству", + "5837": " tab", + "5838": " Wenn", + "5839": "мати", + "5840": " чем", + "5841": "ograf", + "5842": "πή", + "5843": " ceea", + "5844": " mont", + "5845": " întâ", + "5846": " знаю", + "5847": " Sk", + "5848": " äh", + "5849": " projekt", + "5850": "вим", + "5851": " demok", + "5852": " other", + "5853": " Myslím", + "5854": "äll", + "5855": " tru", + "5856": " виде", + "5857": " ukoll", + "5858": "ational", + "5859": "íz", + "5860": "ént", + "5861": "дом", + "5862": "olat", + "5863": "ittle", + "5864": " Allura", + "5865": " wirklich", + "5866": "bolj", + "5867": " otro", + "5868": " tylko", + "5869": "ξε", + "5870": "kou", + "5871": "pel", + "5872": "inis", + "5873": " oike", + "5874": "aucoup", + "5875": " place", + "5876": "üs", + "5877": "ун", + "5878": "ħda", + "5879": " zum", + "5880": " jaun", + "5881": "áte", + "5882": "itti", + "5883": " bos", + "5884": "imos", + "5885": "лкова", + "5886": " rész", + "5887": "icz", + "5888": "skat", + "5889": " termin", + "5890": "țe", + "5891": "ħna", + "5892": " hay", + "5893": " hall", + "5894": "δι", + "5895": "iais", + "5896": " συν", + "5897": "ówi", + "5898": "anno", + "5899": " этом", + "5900": " ollut", + "5901": "udo", + "5902": "unto", + "5903": "клад", + "5904": "óż", + "5905": "ęd", + "5906": "ρίζ", + "5907": "ināt", + "5908": "sem", + "5909": "нні", + "5910": " Sc", + "5911": " sad", + "5912": " pur", + "5913": "rå", + "5914": "ползва", + "5915": " Wo", + "5916": "tö", + "5917": " Din", + "5918": " imm", + "5919": " vyt", + "5920": " мога", + "5921": "log", + "5922": " въз", + "5923": " уве", + "5924": "inder", + "5925": " prad", + "5926": " αρ", + "5927": "enda", + "5928": " acum", + "5929": " свои", + "5930": " obser", + "5931": " naħseb", + "5932": "ън", + "5933": " dawn", + "5934": " viss", + "5935": "ležit", + "5936": "ief", + "5937": "ход", + "5938": "ющи", + "5939": "áj", + "5940": " Mer", + "5941": " usp", + "5942": "denti", + "5943": " aqui", + "5944": " bych", + "5945": " більше", + "5946": "unud", + "5947": "ún", + "5948": "nar", + "5949": "рес", + "5950": " dů", + "5951": " κό", + "5952": " fos", + "5953": " padar", + "5954": "ίζω", + "5955": " lí", + "5956": "anju", + "5957": " einfach", + "5958": "ios", + "5959": "rii", + "5960": "owi", + "5961": "iger", + "5962": "ával", + "5963": "ούμε", + "5964": " lat", + "5965": "ću", + "5966": "inote", + "5967": " materi", + "5968": " Pal", + "5969": " fie", + "5970": " pic", + "5971": "ciu", + "5972": "tos", + "5973": " når", + "5974": " byla", + "5975": " nast", + "5976": " jų", + "5977": " Tor", + "5978": "heden", + "5979": " poder", + "5980": " because", + "5981": " который", + "5982": "āja", + "5983": "ční", + "5984": " important", + "5985": "ekom", + "5986": " Ser", + "5987": "λί", + "5988": " Χ", + "5989": " För", + "5990": " mj", + "5991": "nici", + "5992": "вания", + "5993": " вла", + "5994": " Lond", + "5995": " wsp", + "5996": "зь", + "5997": " sens", + "5998": " način", + "5999": "ρυ", + "6000": "ються", + "6001": " estão", + "6002": " potom", + "6003": "đu", + "6004": "zés", + "6005": "лич", + "6006": " hund", + "6007": "üt", + "6008": "ший", + "6009": " gł", + "6010": " спра", + "6011": "iekš", + "6012": " honom", + "6013": "ationen", + "6014": "нов", + "6015": "ρετε", + "6016": "omo", + "6017": "zor", + "6018": " blo", + "6019": " rok", + "6020": " още", + "6021": " rela", + "6022": " μεγά", + "6023": "ēd", + "6024": " rak", + "6025": " διά", + "6026": " μόνο", + "6027": "ков", + "6028": " Igen", + "6029": " norm", + "6030": "unca", + "6031": " dor", + "6032": " Mens", + "6033": "ült", + "6034": "ėtų", + "6035": " Jā", + "6036": " Dus", + "6037": " jim", + "6038": " пов", + "6039": "ará", + "6040": " sí", + "6041": " jät", + "6042": "ppo", + "6043": "ття", + "6044": " Gu", + "6045": " наи", + "6046": " clas", + "6047": " zato", + "6048": " tomto", + "6049": " web", + "6050": "aug", + "6051": "olt", + "6052": " liik", + "6053": "wy", + "6054": "ге", + "6055": "ною", + "6056": " rää", + "6057": " χρό", + "6058": " sina", + "6059": " kontrol", + "6060": "aram", + "6061": " jkun", + "6062": " gebru", + "6063": " толкова", + "6064": "ian", + "6065": "amise", + "6066": " două", + "6067": " võib", + "6068": "ικέ", + "6069": " ét", + "6070": " lor", + "6071": " ingen", + "6072": " nende", + "6073": "ški", + "6074": "amaan", + "6075": " orig", + "6076": " голо", + "6077": " tutti", + "6078": " может", + "6079": "dag", + "6080": " Θα", + "6081": "kken", + "6082": " poh", + "6083": "ontos", + "6084": "jší", + "6085": "ikti", + "6086": "мери", + "6087": " tuo", + "6088": " seus", + "6089": " λί", + "6090": " supp", + "6091": "кс", + "6092": "ilu", + "6093": " vro", + "6094": "wię", + "6095": " Ev", + "6096": " τόσο", + "6097": " преди", + "6098": " två", + "6099": " zwe", + "6100": " putea", + "6101": " probab", + "6102": "обре", + "6103": " mõt", + "6104": "ember", + "6105": " gøre", + "6106": " zdrav", + "6107": "κόμα", + "6108": " liel", + "6109": " ostat", + "6110": " toate", + "6111": " zp", + "6112": " hit", + "6113": " tras", + "6114": "bel", + "6115": " milli", + "6116": " beaucoup", + "6117": "ются", + "6118": "řed", + "6119": "iskt", + "6120": " noi", + "6121": " περί", + "6122": " jó", + "6123": " pré", + "6124": " Dies", + "6125": " contra", + "6126": " только", + "6127": "fo", + "6128": " long", + "6129": " tijd", + "6130": " divers", + "6131": " tým", + "6132": " χρη", + "6133": " għax", + "6134": " poate", + "6135": "gio", + "6136": "ovala", + "6137": " apenas", + "6138": " look", + "6139": " kuris", + "6140": " talál", + "6141": "owy", + "6142": "šta", + "6143": "лити", + "6144": " Για", + "6145": " ско", + "6146": " Mich", + "6147": " lill", + "6148": " wieħed", + "6149": ",.", + "6150": " ją", + "6151": " lö", + "6152": "unos", + "6153": " moj", + "6154": "ėra", + "6155": " stop", + "6156": "šķ", + "6157": " rés", + "6158": " praw", + "6159": "set", + "6160": "elet", + "6161": " mia", + "6162": " paz", + "6163": " immer", + "6164": "sl", + "6165": " vin", + "6166": " antes", + "6167": " could", + "6168": " συνέ", + "6169": "imy", + "6170": "orth", + "6171": "ложе", + "6172": " region", + "6173": "ése", + "6174": "alle", + "6175": "iaus", + "6176": "unci", + "6177": "ържа", + "6178": " mine", + "6179": "udu", + "6180": " sas", + "6181": "ordan", + "6182": " work", + "6183": " kohta", + "6184": " исполь", + "6185": "part", + "6186": "ások", + "6187": "чите", + "6188": " gewo", + "6189": " trimest", + "6190": " kän", + "6191": "ными", + "6192": " Sin", + "6193": " proc", + "6194": " financi", + "6195": " Gra", + "6196": " vom", + "6197": " могу", + "6198": " Β", + "6199": "jad", + "6200": "ksen", + "6201": " twee", + "6202": "łą", + "6203": "kiem", + "6204": "vern", + "6205": " Jer", + "6206": " jou", + "6207": " zi", + "6208": "oses", + "6209": "ēram", + "6210": " roh", + "6211": " vig", + "6212": " meine", + "6213": "uf", + "6214": "γε", + "6215": "onu", + "6216": " nj", + "6217": "eken", + "6218": "σιμο", + "6219": " Tako", + "6220": "utu", + "6221": " Ну", + "6222": " All", + "6223": " αυτά", + "6224": "ρει", + "6225": "áva", + "6226": " estamos", + "6227": "ibil", + "6228": " nici", + "6229": "øj", + "6230": "anu", + "6231": "ριο", + "6232": "ické", + "6233": "igur", + "6234": "mény", + "6235": " Gal", + "6236": " complet", + "6237": "ėk", + "6238": "ин", + "6239": " dl", + "6240": "utoč", + "6241": " jus", + "6242": " σχ", + "6243": "skih", + "6244": " σχε", + "6245": "tions", + "6246": "кан", + "6247": " chiar", + "6248": "бя", + "6249": "daj", + "6250": "pun", + "6251": "εία", + "6252": "erk", + "6253": " Åh", + "6254": " Mit", + "6255": " aga", + "6256": " cro", + "6257": "ameni", + "6258": " viac", + "6259": " fordi", + "6260": "nte", + "6261": "son", + "6262": "čne", + "6263": " Finn", + "6264": " vå", + "6265": "преде", + "6266": " sing", + "6267": " больше", + "6268": " how", + "6269": " tagħha", + "6270": "āka", + "6271": "вид", + "6272": " ор", + "6273": " enor", + "6274": " rece", + "6275": " неща", + "6276": "fl", + "6277": " minä", + "6278": " genom", + "6279": " velmi", + "6280": "řeb", + "6281": "рат", + "6282": "ците", + "6283": " еще", + "6284": " fund", + "6285": " ug", + "6286": "pect", + "6287": " Aga", + "6288": " sod", + "6289": " zgod", + "6290": " žmon", + "6291": "uth", + "6292": "nings", + "6293": " služ", + "6294": " iar", + "6295": " Parlament", + "6296": "θέ", + "6297": "ály", + "6298": "μέν", + "6299": " která", + "6300": "iens", + "6301": "mist", + "6302": "лись", + "6303": "ury", + "6304": "rati", + "6305": " fatto", + "6306": " sedan", + "6307": "čí", + "6308": "ough", + "6309": " esmu", + "6310": " какво", + "6311": " tiedätkö", + "6312": "iller", + "6313": " част", + "6314": "ical", + "6315": " konf", + "6316": " quanto", + "6317": "ated", + "6318": " Tre", + "6319": "этому", + "6320": " druh", + "6321": " παρά", + "6322": "нию", + "6323": " ше", + "6324": " ihre", + "6325": "olj", + "6326": " înc", + "6327": " jaoks", + "6328": " πράγμα", + "6329": " bev", + "6330": " zan", + "6331": " bere", + "6332": " right", + "6333": " ezt", + "6334": " tika", + "6335": " dobre", + "6336": " tus", + "6337": " čia", + "6338": "mt", + "6339": "žno", + "6340": " lot", + "6341": " nám", + "6342": " ener", + "6343": " przed", + "6344": "kum", + "6345": " sé", + "6346": " profes", + "6347": "elem", + "6348": " indiv", + "6349": "ßen", + "6350": "яви", + "6351": " Għal", + "6352": " paar", + "6353": " piem", + "6354": "isas", + "6355": " kah", + "6356": " osa", + "6357": " znate", + "6358": " Iżda", + "6359": " trochu", + "6360": " Uni", + "6361": " jista", + "6362": " ktorá", + "6363": " procent", + "6364": "ähr", + "6365": "znál", + "6366": " fue", + "6367": "ството", + "6368": " követ", + "6369": "ність", + "6370": " резу", + "6371": " public", + "6372": "raš", + "6373": " Pat", + "6374": " Ναι", + "6375": " няма", + "6376": "ům", + "6377": "enty", + "6378": "стру", + "6379": " cada", + "6380": "geb", + "6381": "qas", + "6382": "nica", + "6383": " Dat", + "6384": "iselt", + "6385": "arm", + "6386": " πά", + "6387": "inger", + "6388": "χι", + "6389": "čné", + "6390": " mest", + "6391": " dieser", + "6392": "yer", + "6393": " fö", + "6394": " нужно", + "6395": "vaj", + "6396": "нан", + "6397": "asse", + "6398": "stat", + "6399": "лися", + "6400": "мата", + "6401": "entar", + "6402": " kn", + "6403": " Cre", + "6404": " uporab", + "6405": "oren", + "6406": " παι", + "6407": " było", + "6408": " activ", + "6409": "kod", + "6410": " ky", + "6411": "лата", + "6412": "lichen", + "6413": "πι", + "6414": " συνε", + "6415": "igo", + "6416": "tava", + "6417": "érie", + "6418": " міс", + "6419": " dizer", + "6420": " midagi", + "6421": "wać", + "6422": " ця", + "6423": " mille", + "6424": "των", + "6425": "inės", + "6426": " zei", + "6427": "знача", + "6428": "nje", + "6429": "īga", + "6430": " Kol", + "6431": " rav", + "6432": " tip", + "6433": " pozn", + "6434": " jeśli", + "6435": " nadal", + "6436": " bef", + "6437": " fis", + "6438": " kunt", + "6439": "kam", + "6440": " dam", + "6441": "ortun", + "6442": " можу", + "6443": "čil", + "6444": " hus", + "6445": " teis", + "6446": "ateľ", + "6447": " Fre", + "6448": " cop", + "6449": " сти", + "6450": " commen", + "6451": " online", + "6452": " tant", + "6453": "mus", + "6454": "vät", + "6455": "жливо", + "6456": " glas", + "6457": " suure", + "6458": " Sí", + "6459": " aici", + "6460": " kasut", + "6461": "kri", + "6462": " Toda", + "6463": " jste", + "6464": "ņēm", + "6465": "ším", + "6466": "jest", + "6467": " uni", + "6468": " tous", + "6469": " μά", + "6470": "anne", + "6471": " Pos", + "6472": " Teraz", + "6473": "ső", + "6474": " Sm", + "6475": " stav", + "6476": "pal", + "6477": "вал", + "6478": " κυ", + "6479": " ки", + "6480": " Cor", + "6481": " sex", + "6482": " spole", + "6483": " produkt", + "6484": " gewoon", + "6485": " social", + "6486": "pär", + "6487": "ére", + "6488": " sf", + "6489": " lju", + "6490": " põh", + "6491": " rid", + "6492": " može", + "6493": " tilb", + "6494": "ements", + "6495": " Αν", + "6496": "ogle", + "6497": "vous", + "6498": " maks", + "6499": " system", + "6500": "under", + "6501": " aquí", + "6502": " jaar", + "6503": " sign", + "6504": " empres", + "6505": "ának", + "6506": " ανά", + "6507": " back", + "6508": " была", + "6509": "lex", + "6510": " fü", + "6511": " etwas", + "6512": " май", + "6513": " Face", + "6514": " YouT", + "6515": " fejn", + "6516": "ektr", + "6517": "aix", + "6518": "unde", + "6519": " šeit", + "6520": " arbej", + "6521": " jav", + "6522": "zioni", + "6523": " систе", + "6524": " използва", + "6525": "ye", + "6526": "вят", + "6527": " mé", + "6528": " yh", + "6529": "adores", + "6530": " diver", + "6531": " menos", + "6532": " kuidas", + "6533": "hez", + "6534": "čit", + "6535": "ální", + "6536": "ваме", + "6537": "tar", + "6538": "чин", + "6539": "fte", + "6540": "цій", + "6541": " úl", + "6542": " idő", + "6543": " mange", + "6544": "elig", + "6545": "мага", + "6546": " bola", + "6547": " пови", + "6548": "bles", + "6549": " mnog", + "6550": " heidän", + "6551": "wią", + "6552": " Kr", + "6553": " bodo", + "6554": "ege", + "6555": "plu", + "6556": "вий", + "6557": "телно", + "6558": "imu", + "6559": "unn", + "6560": "tir", + "6561": "ντα", + "6562": "ător", + "6563": " εκεί", + "6564": " velik", + "6565": " būs", + "6566": " Unter", + "6567": " autor", + "6568": " identi", + "6569": " sitten", + "6570": "дже", + "6571": "чко", + "6572": " Wh", + "6573": " atât", + "6574": " sagde", + "6575": "ång", + "6576": " Eh", + "6577": "drig", + "6578": " mám", + "6579": " zoals", + "6580": " diffic", + "6581": "kien", + "6582": " fiz", + "6583": "kelig", + "6584": " vrlo", + "6585": "аа", + "6586": " wid", + "6587": " üle", + "6588": " викори", + "6589": "γου", + "6590": " Czy", + "6591": " cho", + "6592": " fri", + "6593": " тук", + "6594": " pokud", + "6595": " até", + "6596": " jiġ", + "6597": " pid", + "6598": " более", + "6599": "ματο", + "6600": "endre", + "6601": " servi", + "6602": "ātad", + "6603": " πρώ", + "6604": "rz", + "6605": "õt", + "6606": " sw", + "6607": " mur", + "6608": " σαν", + "6609": " rapp", + "6610": " făcut", + "6611": "arijiet", + "6612": " ū", + "6613": "cel", + "6614": "dru", + "6615": "erta", + "6616": " zeg", + "6617": " issa", + "6618": " pasa", + "6619": " живо", + "6620": " Inter", + "6621": "EN", + "6622": "agu", + "6623": "χει", + "6624": " uw", + "6625": "anut", + "6626": "ander", + "6627": " brit", + "6628": "endant", + "6629": " będzie", + "6630": "dě", + "6631": "нем", + "6632": " ис", + "6633": " була", + "6634": " quindi", + "6635": "rou", + "6636": "чна", + "6637": " itt", + "6638": "bt", + "6639": "lož", + "6640": " Okay", + "6641": " этого", + "6642": "och", + "6643": "ový", + "6644": " tax", + "6645": "стори", + "6646": " wara", + "6647": " бі", + "6648": "etty", + "6649": "ôsob", + "6650": " Can", + "6651": " dve", + "6652": " rep", + "6653": " яка", + "6654": " vaat", + "6655": " ponieważ", + "6656": " alb", + "6657": "tem", + "6658": " hlav", + "6659": " mitte", + "6660": "kre", + "6661": " Hi", + "6662": " UE", + "6663": " coś", + "6664": " jid", + "6665": " При", + "6666": " tehn", + "6667": " ćemo", + "6668": " послед", + "6669": " davvero", + "6670": " Є", + "6671": "ási", + "6672": " ла", + "6673": "ihin", + "6674": " oameni", + "6675": " produk", + "6676": "уу", + "6677": "kaa", + "6678": "пъл", + "6679": " By", + "6680": " допо", + "6681": " nared", + "6682": "ėte", + "6683": "AAAA", + "6684": " ceva", + "6685": " sehen", + "6686": " stále", + "6687": "kuš", + "6688": "eeee", + "6689": "коло", + "6690": " hin", + "6691": " qualc", + "6692": " temps", + "6693": " cha", + "6694": " ora", + "6695": "йти", + "6696": "чита", + "6697": " polí", + "6698": " quar", + "6699": "æg", + "6700": " Oui", + "6701": "rī", + "6702": "con", + "6703": " кри", + "6704": " малко", + "6705": "ége", + "6706": "ħaj", + "6707": "isem", + "6708": "ális", + "6709": " anal", + "6710": " peri", + "6711": " todas", + "6712": "cup", + "6713": " dik", + "6714": " love", + "6715": " нача", + "6716": "ždy", + "6717": "udzi", + "6718": " poco", + "6719": " toen", + "6720": " puede", + "6721": "âr", + "6722": "τεύ", + "6723": " Sta", + "6724": " ach", + "6725": "къ", + "6726": "jeti", + "6727": " Lab", + "6728": " víte", + "6729": " capac", + "6730": "orden", + "6731": " kraj", + "6732": " авто", + "6733": " maail", + "6734": " unser", + "6735": " differen", + "6736": " tý", + "6737": " Aus", + "6738": " työ", + "6739": " oħra", + "6740": " waħda", + "6741": "hin", + "6742": "iejs", + "6743": "óóóó", + "6744": "škai", + "6745": " Kad", + "6746": "форма", + "6747": "IT", + "6748": "jä", + "6749": "ney", + "6750": "inio", + "6751": " West", + "6752": "itter", + "6753": " Zeit", + "6754": " final", + "6755": " tagasi", + "6756": " sellest", + "6757": "enu", + "6758": "вих", + "6759": "itse", + "6760": "liet", + "6761": " свої", + "6762": " utiliz", + "6763": "indo", + "6764": " kaks", + "6765": " daugiau", + "6766": "tia", + "6767": "род", + "6768": "rawdę", + "6769": " bend", + "6770": " diss", + "6771": "sä", + "6772": "itev", + "6773": "ħħar", + "6774": " art", + "6775": " ува", + "6776": " mnie", + "6777": " estar", + "6778": "stå", + "6779": "ádz", + "6780": " Дж", + "6781": " dess", + "6782": " sport", + "6783": " Tar", + "6784": "μμ", + "6785": "inas", + "6786": " Tātad", + "6787": "ρκ", + "6788": "tive", + "6789": " сле", + "6790": " torn", + "6791": " omdat", + "6792": "¡¡", + "6793": "istu", + "6794": " arg", + "6795": " folk", + "6796": " tiempo", + "6797": " Dit", + "6798": " Naj", + "6799": " Como", + "6800": " teha", + "6801": " tört", + "6802": " yhte", + "6803": "iesies", + "6804": " потому", + "6805": " opravdu", + "6806": "βά", + "6807": " Φ", + "6808": "ieš", + "6809": "ikk", + "6810": " kro", + "6811": " Добре", + "6812": "acă", + "6813": "кото", + "6814": " apoi", + "6815": " použ", + "6816": " wkoll", + "6817": " internet", + "6818": " наистина", + "6819": " où", + "6820": "vert", + "6821": "jí", + "6822": "йо", + "6823": "шь", + "6824": "bru", + "6825": "dej", + "6826": "τια", + "6827": " Ty", + "6828": " skr", + "6829": "φέ", + "6830": "liš", + "6831": " good", + "6832": "rs", + "6833": " Sì", + "6834": " mű", + "6835": "ról", + "6836": " ats", + "6837": "heder", + "6838": " Ra", + "6839": " zob", + "6840": "inga", + "6841": " тра", + "6842": "ewwel", + "6843": " where", + "6844": " както", + "6845": " necess", + "6846": " YouTube", + "6847": "вой", + "6848": " govor", + "6849": " tajjeb", + "6850": " лу", + "6851": " mud", + "6852": "вести", + "6853": " aussi", + "6854": " vissz", + "6855": "kyt", + "6856": "rel", + "6857": " áno", + "6858": " paci", + "6859": " κάνει", + "6860": "вого", + "6861": " jul", + "6862": " между", + "6863": "rā", + "6864": "pil", + "6865": "ască", + "6866": " godt", + "6867": " mlad", + "6868": " produc", + "6869": " Ber", + "6870": " škol", + "6871": " povedal", + "6872": "elm", + "6873": "tie", + "6874": " lar", + "6875": " має", + "6876": " bolj", + "6877": " niečo", + "6878": "AN", + "6879": "raž", + "6880": "wad", + "6881": " eb", + "6882": " make", + "6883": " trad", + "6884": " külön", + "6885": "fil", + "6886": "endes", + "6887": "accord", + "6888": " pul", + "6889": " Тепер", + "6890": "--", + "6891": "ús", + "6892": "adt", + "6893": "тре", + "6894": " insp", + "6895": " hovor", + "6896": "atro", + "6897": "лося", + "6898": " Rich", + "6899": " aldrig", + "6900": "ox", + "6901": " Ро", + "6902": "issä", + "6903": " vaš", + "6904": " ďal", + "6905": " пър", + "6906": "ването", + "6907": " politik", + "6908": " dois", + "6909": "eln", + "6910": "кту", + "6911": " cat", + "6912": " zah", + "6913": " тру", + "6914": " nada", + "6915": " két", + "6916": " qué", + "6917": "tac", + "6918": "ять", + "6919": "unta", + "6920": "úúúú", + "6921": "пера", + "6922": "роят", + "6923": " lejn", + "6924": " себе", + "6925": " point", + "6926": "wor", + "6927": " nõ", + "6928": " qal", + "6929": " sum", + "6930": " сре", + "6931": " Imma", + "6932": " nevar", + "6933": "aden", + "6934": " vedno", + "6935": " gol", + "6936": " još", + "6937": " μικ", + "6938": " jaut", + "6939": " mora", + "6940": " potre", + "6941": "ью", + "6942": "лом", + "6943": " Jūs", + "6944": "αααααααα", + "6945": " üh", + "6946": "liche", + "6947": " pien", + "6948": " Со", + "6949": "enea", + "6950": "μένη", + "6951": " ots", + "6952": " žin", + "6953": " histor", + "6954": " skutoč", + "6955": "ėd", + "6956": "nisk", + "6957": "rata", + "6958": "ките", + "6959": " Van", + "6960": " vän", + "6961": " besz", + "6962": " Са", + "6963": " нав", + "6964": "lement", + "6965": " agora", + "6966": "ΟΟ", + "6967": "dig", + "6968": "vie", + "6969": " fung", + "6970": " nors", + "6971": " toga", + "6972": " predstav", + "6973": "cri", + "6974": "kra", + "6975": " sagte", + "6976": " veste", + "6977": "щу", + "6978": "čan", + "6979": "сна", + "6980": " prijat", + "6981": "uren", + "6982": " list", + "6983": " fic", + "6984": " Finnish", + "6985": " tikrųjų", + "6986": "viz", + "6987": " сер", + "6988": " fast", + "6989": " posta", + "6990": " seis", + "6991": " många", + "6992": "лей", + "6993": " Sz", + "6994": " спе", + "6995": " labi", + "6996": " tehd", + "6997": " получи", + "6998": " bih", + "6999": "sioon", + "7000": " mucho", + "7001": " parlament", + "7002": "фі", + "7003": "жде", + "7004": " jä", + "7005": " civ", + "7006": " vic", + "7007": " aika", + "7008": " neid", + "7009": "desmit", + "7010": " seine", + "7011": " njegov", + "7012": " profess", + "7013": "зно", + "7014": " От", + "7015": "лове", + "7016": "jorde", + "7017": " Olen", + "7018": " fors", + "7019": "enz", + "7020": "σκε", + "7021": "спе", + "7022": " anc", + "7023": " olisi", + "7024": "ület", + "7025": " juh", + "7026": " lov", + "7027": " nue", + "7028": " až", + "7029": " ży", + "7030": "asid", + "7031": " ihn", + "7032": " imag", + "7033": " note", + "7034": " noti", + "7035": " eivät", + "7036": " jetzt", + "7037": "elikult", + "7038": "SSSS", + "7039": "вала", + "7040": " sla", + "7041": " slut", + "7042": " обе", + "7043": "manto", + "7044": " după", + "7045": " urmă", + "7046": " keine", + "7047": " ακόμα", + "7048": "bild", + "7049": "erte", + "7050": "itik", + "7051": " нико", + "7052": " little", + "7053": " virkelig", + "7054": " sì", + "7055": " pob", + "7056": " tím", + "7057": " тро", + "7058": "шение", + "7059": " indi", + "7060": " miejs", + "7061": "σια", + "7062": "isko", + "7063": " nal", + "7064": "aħħar", + "7065": " zdaj", + "7066": " gyven", + "7067": "тро", + "7068": " lø", + "7069": "ható", + "7070": " Een", + "7071": "στό", + "7072": " пар", + "7073": " μετά", + "7074": "stva", + "7075": "emaal", + "7076": "ových", + "7077": "qt", + "7078": "edy", + "7079": " ano", + "7080": " lop", + "7081": "oliko", + "7082": " tieto", + "7083": "äd", + "7084": "žas", + "7085": "тім", + "7086": "нете", + "7087": " hak", + "7088": " αρχ", + "7089": " szem", + "7090": " spolo", + "7091": " Facebo", + "7092": "ómo", + "7093": "znam", + "7094": " Nap", + "7095": "лення", + "7096": " Pres", + "7097": " nėra", + "7098": " than", + "7099": "ellement", + "7100": "bir", + "7101": "kke", + "7102": "pir", + "7103": "ulta", + "7104": " Wie", + "7105": " lon", + "7106": " tämän", + "7107": "аль", + "7108": "alne", + "7109": "timo", + "7110": "като", + "7111": " rah", + "7112": " uděl", + "7113": "ids", + "7114": "äch", + "7115": " andre", + "7116": " besch", + "7117": " nombre", + "7118": "ago", + "7119": "ível", + "7120": "μένα", + "7121": " орга", + "7122": " nogle", + "7123": " respons", + "7124": "ef", + "7125": "xim", + "7126": " оби", + "7127": " poch", + "7128": "ningar", + "7129": " pamat", + "7130": " pasak", + "7131": " mė", + "7132": " ens", + "7133": " mär", + "7134": " char", + "7135": " någon", + "7136": " tiene", + "7137": "pot", + "7138": "ffff", + "7139": "šana", + "7140": " ran", + "7141": "μαστε", + "7142": " tunn", + "7143": "taient", + "7144": " favor", + "7145": " različ", + "7146": " знаєте", + "7147": " зав", + "7148": " blij", + "7149": " sama", + "7150": " друго", + "7151": " alleen", + "7152": "ath", + "7153": "esn", + "7154": "pués", + "7155": " děl", + "7156": "lek", + "7157": "ško", + "7158": "дар", + "7159": "зни", + "7160": " yo", + "7161": " Ben", + "7162": "oms", + "7163": " něk", + "7164": " Brit", + "7165": " esam", + "7166": " lite", + "7167": " podr", + "7168": " pois", + "7169": " mesmo", + "7170": " todella", + "7171": "ieden", + "7172": " игра", + "7173": "esed", + "7174": " mali", + "7175": "ivad", + "7176": " skute", + "7177": "aid", + "7178": "āks", + "7179": " mysl", + "7180": " maken", + "7181": " прода", + "7182": "χαν", + "7183": " том", + "7184": "aires", + "7185": " fois", + "7186": " γιατί", + "7187": " rzeczy", + "7188": "jus", + "7189": "τών", + "7190": " Gesch", + "7191": " komun", + "7192": "erde", + "7193": " ici", + "7194": " nin", + "7195": " way", + "7196": " дея", + "7197": " lors", + "7198": " premi", + "7199": "лень", + "7200": " vasta", + "7201": "cen", + "7202": " ahora", + "7203": "omi", + "7204": "στά", + "7205": " dzī", + "7206": " say", + "7207": " daar", + "7208": " проду", + "7209": " unsere", + "7210": "bro", + "7211": "ħor", + "7212": "θρω", + "7213": "λεί", + "7214": " Го", + "7215": " rat", + "7216": " své", + "7217": " diesem", + "7218": " потрібно", + "7219": "κά", + "7220": "kao", + "7221": " εγ", + "7222": "vest", + "7223": " vou", + "7224": " control", + "7225": "ħu", + "7226": " dí", + "7227": " δι", + "7228": "iała", + "7229": " jelent", + "7230": " tikrai", + "7231": " Dž", + "7232": " puol", + "7233": " Europ", + "7234": " Kyllä", + "7235": " означа", + "7236": "ides", + "7237": " Mac", + "7238": " cou", + "7239": " mio", + "7240": " тем", + "7241": " свою", + "7242": " ν", + "7243": "éné", + "7244": " Kuid", + "7245": " first", + "7246": "διά", + "7247": " olid", + "7248": " vamos", + "7249": " sistem", + "7250": " bliver", + "7251": " asemenea", + "7252": "ajt", + "7253": "anos", + "7254": "ження", + "7255": " esse", + "7256": " pomo", + "7257": " telj", + "7258": " tudod", + "7259": "вительно", + "7260": "ното", + "7261": " apm", + "7262": "ienia", + "7263": " klaus", + "7264": " hö", + "7265": " viv", + "7266": "ból", + "7267": " λέ", + "7268": " rol", + "7269": " tah", + "7270": "глежда", + "7271": " några", + "7272": " europe", + "7273": "omm", + "7274": "rir", + "7275": " pala", + "7276": " Univers", + "7277": "ős", + "7278": "unut", + "7279": "лось", + "7280": " müs", + "7281": "чо", + "7282": "oki", + "7283": "eron", + "7284": "idel", + "7285": " ese", + "7286": " band", + "7287": " Nii", + "7288": " svi", + "7289": "aktar", + "7290": " správ", + "7291": " vastu", + "7292": "ssz", + "7293": " ps", + "7294": " están", + "7295": "σιμοποι", + "7296": " postav", + "7297": " ću", + "7298": "alta", + "7299": "cios", + "7300": " euh", + "7301": " продъл", + "7302": " puh", + "7303": "emmän", + "7304": "čnost", + "7305": " Port", + "7306": " '", + "7307": "sled", + "7308": " clar", + "7309": " Dakle", + "7310": " kapcs", + "7311": " elektr", + "7312": " origin", + "7313": "ês", + "7314": " Úgy", + "7315": " radi", + "7316": " вече", + "7317": " market", + "7318": "χο", + "7319": " Sir", + "7320": " обо", + "7321": " Willi", + "7322": " nista", + "7323": "amm", + "7324": "ulo", + "7325": "ńst", + "7326": "atel", + "7327": "ogen", + "7328": "viet", + "7329": " Sen", + "7330": " tiež", + "7331": " sistema", + "7332": "gev", + "7333": "iat", + "7334": "ėje", + "7335": " acho", + "7336": " инте", + "7337": " medic", + "7338": "οο", + "7339": "inde", + "7340": "зова", + "7341": " подо", + "7342": " volta", + "7343": "olg", + "7344": " lá", + "7345": " Tra", + "7346": "yg", + "7347": "eko", + "7348": "rio", + "7349": "ное", + "7350": "ffen", + "7351": "stan", + "7352": " halu", + "7353": "zon", + "7354": " jgħ", + "7355": " dollar", + "7356": "xxxx", + "7357": " kall", + "7358": " volna", + "7359": " oblasti", + "7360": "кон", + "7361": " well", + "7362": " projek", + "7363": " naprawdę", + "7364": "τεί", + "7365": "νεται", + "7366": " hran", + "7367": " trav", + "7368": " institu", + "7369": " Ва", + "7370": " czę", + "7371": " cose", + "7372": "eds", + "7373": " Stre", + "7374": "éz", + "7375": " sun", + "7376": " down", + "7377": " fak", + "7378": " sle", + "7379": " päiv", + "7380": " yrit", + "7381": " день", + "7382": "dob", + "7383": " add", + "7384": " fej", + "7385": " país", + "7386": " iż", + "7387": " Así", + "7388": "amais", + "7389": " esim", + "7390": " kuten", + "7391": "лни", + "7392": "ingu", + "7393": " Jap", + "7394": "пы", + "7395": "зви", + "7396": " наше", + "7397": " somet", + "7398": " tāpēc", + "7399": " gjorde", + "7400": "roš", + "7401": "čně", + "7402": " γε", + "7403": " νέ", + "7404": " typ", + "7405": " onde", + "7406": " одно", + "7407": " anden", + "7408": "сен", + "7409": "tiva", + "7410": " lad", + "7411": " части", + "7412": "ък", + "7413": " θε", + "7414": " cet", + "7415": " pela", + "7416": " Τα", + "7417": "ativa", + "7418": " další", + "7419": " nějak", + "7420": " повер", + "7421": "žd", + "7422": "ność", + "7423": " bár", + "7424": " rov", + "7425": " σου", + "7426": "ности", + "7427": " vari", + "7428": " anderen", + "7429": "née", + "7430": "atos", + "7431": "elta", + "7432": " rast", + "7433": " blive", + "7434": " gesch", + "7435": "ozaj", + "7436": " ton", + "7437": " chod", + "7438": " зали", + "7439": " Vis", + "7440": " impl", + "7441": " pärast", + "7442": " Menschen", + "7443": "ők", + "7444": "διο", + "7445": "endi", + "7446": " niż", + "7447": "ises", + "7448": "нови", + "7449": " dij", + "7450": " ros", + "7451": " robi", + "7452": " estaba", + "7453": " allt", + "7454": " suas", + "7455": "sew", + "7456": " Ci", + "7457": "ville", + "7458": " sada", + "7459": "ността", + "7460": " naozaj", + "7461": "нок", + "7462": "otto", + "7463": "žeme", + "7464": " Hon", + "7465": " kalb", + "7466": " pati", + "7467": " wiesz", + "7468": "кою", + "7469": " сп", + "7470": "ният", + "7471": " kie", + "7472": " sre", + "7473": "вайте", + "7474": " aast", + "7475": " varētu", + "7476": "fal", + "7477": "ieb", + "7478": " kér", + "7479": " inde", + "7480": "λογ", + "7481": " åt", + "7482": " Па", + "7483": "ário", + "7484": " jit", + "7485": "amine", + "7486": "ligen", + "7487": " arvan", + "7488": " maintenant", + "7489": "inę", + "7490": " ша", + "7491": " Sel", + "7492": " anim", + "7493": " igaz", + "7494": " määr", + "7495": " comer", + "7496": " opera", + "7497": "γω", + "7498": " mov", + "7499": " zach", + "7500": " posled", + "7501": " administ", + "7502": "ety", + "7503": " ieg", + "7504": " würde", + "7505": "ără", + "7506": " Vor", + "7507": "akter", + "7508": "publi", + "7509": " mundo", + "7510": " nunca", + "7511": " sł", + "7512": " благо", + "7513": "ūk", + "7514": "tri", + "7515": "seen", + "7516": " Fin", + "7517": "ħabba", + "7518": " sitt", + "7519": "wet", + "7520": " tarko", + "7521": "вете", + "7522": " kau", + "7523": " чове", + "7524": " resta", + "7525": " nastav", + "7526": " element", + "7527": "ual", + "7528": " хм", + "7529": "ėjau", + "7530": "льта", + "7531": " gov", + "7532": " näh", + "7533": " tada", + "7534": " člov", + "7535": "cev", + "7536": "cza", + "7537": "koj", + "7538": "čio", + "7539": "aliza", + "7540": " assim", + "7541": "єю", + "7542": "дра", + "7543": "лото", + "7544": " bran", + "7545": " nüüd", + "7546": "ninger", + "7547": "kę", + "7548": "лите", + "7549": " бли", + "7550": "andid", + "7551": " hyvä", + "7552": " máme", + "7553": " tilbage", + "7554": "rede", + "7555": " nič", + "7556": " Euró", + "7557": " kiek", + "7558": " jedno", + "7559": " God", + "7560": " maz", + "7561": " tog", + "7562": " vit", + "7563": " кли", + "7564": "iseen", + "7565": " arba", + "7566": "νια", + "7567": " Ti", + "7568": "bile", + "7569": " τρό", + "7570": "ennem", + "7571": " Zato", + "7572": " saat", + "7573": " Bl", + "7574": " aki", + "7575": " ier", + "7576": " nekoliko", + "7577": "ník", + "7578": "tivo", + "7579": "ками", + "7580": " Dur", + "7581": " iem", + "7582": "цията", + "7583": " tová", + "7584": " οικο", + "7585": " робо", + "7586": " хоче", + "7587": "yttä", + "7588": " Net", + "7589": " pelo", + "7590": " minist", + "7591": " Ή", + "7592": "τικέ", + "7593": " Star", + "7594": " lige", + "7595": " Jeśli", + "7596": "ect", + "7597": " rit", + "7598": " perf", + "7599": "uru", + "7600": "đer", + "7601": "itus", + "7602": "ктив", + "7603": " That", + "7604": " prieš", + "7605": " wissen", + "7606": "apa", + "7607": "ogo", + "7608": " incl", + "7609": " avoir", + "7610": " mennes", + "7611": " mindig", + "7612": "hány", + "7613": " jut", + "7614": " sido", + "7615": " decir", + "7616": " kapit", + "7617": "šla", + "7618": " Ye", + "7619": " ori", + "7620": "tavat", + "7621": "zenia", + "7622": " Dru", + "7623": " поч", + "7624": "ěd", + "7625": " sir", + "7626": " iets", + "7627": "ság", + "7628": "шим", + "7629": "ikus", + "7630": "lege", + "7631": " sätt", + "7632": " cosas", + "7633": " coisas", + "7634": "daw", + "7635": "ješ", + "7636": "čaj", + "7637": "eben", + "7638": " weg", + "7639": " tend", + "7640": "ąd", + "7641": "πέ", + "7642": "zion", + "7643": " kép", + "7644": " naf", + "7645": " grad", + "7646": " mówi", + "7647": " pozi", + "7648": " certa", + "7649": " nieko", + "7650": " puțin", + "7651": " zí", + "7652": " trou", + "7653": " trebui", + "7654": "oru", + "7655": "ensä", + "7656": "jena", + "7657": " mama", + "7658": " ellen", + "7659": "üks", + "7660": " Εν", + "7661": "šimt", + "7662": "uksen", + "7663": " għad", + "7664": " saada", + "7665": " eftersom", + "7666": " personas", + "7667": "δί", + "7668": "šet", + "7669": "edes", + "7670": "lust", + "7671": " hang", + "7672": " signific", + "7673": " вз", + "7674": "erst", + "7675": " Sal", + "7676": "gte", + "7677": "inc", + "7678": " îl", + "7679": "sche", + "7680": "ůsob", + "7681": "pportun", + "7682": " podpor", + "7683": " può", + "7684": "ystem", + "7685": "änder", + "7686": " izmanto", + "7687": "ζί", + "7688": "asa", + "7689": "ote", + "7690": " мя", + "7691": "rukt", + "7692": " tehdä", + "7693": " zrobi", + "7694": " či", + "7695": " aps", + "7696": "idente", + "7697": " const", + "7698": " rohkem", + "7699": " χρησιμοποι", + "7700": "от", + "7701": "ква", + "7702": "τηση", + "7703": " Mil", + "7704": " кла", + "7705": "kollu", + "7706": " tych", + "7707": " desde", + "7708": " ktorí", + "7709": "šel", + "7710": " næ", + "7711": "ijek", + "7712": " esp", + "7713": " спа", + "7714": " чув", + "7715": " leid", + "7716": " svoju", + "7717": "umb", + "7718": " Де", + "7719": " ос", + "7720": "ches", + "7721": "eles", + "7722": "ложи", + "7723": " edu", + "7724": " sko", + "7725": " dove", + "7726": "κή", + "7727": "emy", + "7728": "imin", + "7729": "iski", + "7730": "unden", + "7731": " Para", + "7732": " noss", + "7733": " teie", + "7734": " dokon", + "7735": " europ", + "7736": "turi", + "7737": " zac", + "7738": " εργ", + "7739": "enten", + "7740": " veik", + "7741": " våra", + "7742": "zw", + "7743": "ža", + "7744": "osť", + "7745": "aniu", + "7746": " När", + "7747": "eller", + "7748": " spun", + "7749": " hyvin", + "7750": " ihmis", + "7751": " истори", + "7752": "nil", + "7753": " žen", + "7754": " ming", + "7755": "χω", + "7756": " tá", + "7757": "etek", + "7758": "нова", + "7759": " pă", + "7760": "tiin", + "7761": " bomo", + "7762": " cerc", + "7763": " καθώ", + "7764": " ancora", + "7765": " έχουμε", + "7766": "кли", + "7767": "aval", + "7768": " noz", + "7769": "allen", + "7770": " gled", + "7771": " magħ", + "7772": " этот", + "7773": "js", + "7774": "тни", + "7775": " cie", + "7776": " práv", + "7777": " mejor", + "7778": " posit", + "7779": " prest", + "7780": " уча", + "7781": " veľa", + "7782": " sulla", + "7783": "imų", + "7784": "akin", + "7785": " App", + "7786": " смо", + "7787": " olnud", + "7788": " există", + "7789": " realiz", + "7790": "jos", + "7791": "λοι", + "7792": " бъ", + "7793": "tség", + "7794": "мите", + "7795": " pár", + "7796": " tudo", + "7797": " nikdy", + "7798": " spôsob", + "7799": "dien", + "7800": " Kdy", + "7801": " aller", + "7802": "ствительно", + "7803": "ňa", + "7804": "kul", + "7805": "ély", + "7806": "čka", + "7807": " anos", + "7808": "etu", + "7809": "acji", + "7810": "onių", + "7811": " mij", + "7812": "тем", + "7813": "ness", + "7814": " Ils", + "7815": " Jie", + "7816": " gem", + "7817": " zelf", + "7818": " явля", + "7819": " valst", + "7820": "nat", + "7821": " Op", + "7822": " već", + "7823": " фор", + "7824": " Bill", + "7825": " nakon", + "7826": " υπάρχει", + "7827": "omu", + "7828": " äl", + "7829": "iego", + "7830": " Che", + "7831": " gut", + "7832": " kész", + "7833": " проце", + "7834": "ņa", + "7835": " Ще", + "7836": "jort", + "7837": "ljiv", + "7838": " cij", + "7839": " weil", + "7840": " müssen", + "7841": "ць", + "7842": "gue", + "7843": " tuk", + "7844": " vár", + "7845": " parl", + "7846": " τώρα", + "7847": " хочу", + "7848": "ślę", + "7849": "μού", + "7850": " only", + "7851": " őket", + "7852": "alment", + "7853": " model", + "7854": " todėl", + "7855": " használ", + "7856": "ettu", + "7857": " pew", + "7858": " tež", + "7859": " keres", + "7860": " pogled", + "7861": " програ", + "7862": " telefon", + "7863": " ", + "7864": "e", + "7865": "a", + "7866": "i", + "7867": "n", + "7868": "t", + "7869": "o", + "7870": "s", + "7871": "r", + "7872": "l", + "7873": "u", + "7874": "d", + "7875": "m", + "7876": "k", + "7877": ",", + "7878": "p", + "7879": "v", + "7880": "c", + "7881": "g", + "7882": "h", + "7883": ".", + "7884": "j", + "7885": "о", + "7886": "b", + "7887": "а", + "7888": "z", + "7889": "е", + "7890": "и", + "7891": "т", + "7892": "н", + "7893": "y", + "7894": "f", + "7895": "в", + "7896": "р", + "7897": "с", + "7898": "w", + "7899": "α", + "7900": "л", + "7901": "к", + "7902": "д", + "7903": "ä", + "7904": "м", + "7905": "τ", + "7906": "é", + "7907": "ο", + "7908": "ε", + "7909": "п", + "7910": "á", + "7911": "ι", + "7912": "у", + "7913": "ν", + "7914": "я", + "7915": "з", + "7916": "š", + "7917": "і", + "7918": "-", + "7919": "q", + "7920": "í", + "7921": "б", + "7922": "σ", + "7923": "S", + "7924": "A", + "7925": "ρ", + "7926": "ž", + "7927": "г", + "7928": "π", + "7929": "ч", + "7930": "č", + "7931": "'", + "7932": "μ", + "7933": "ā", + "7934": "T", + "7935": "κ", + "7936": "E", + "7937": "M", + "7938": "υ", + "7939": "ă", + "7940": "ö", + "7941": "ь", + "7942": "D", + "7943": "ħ", + "7944": "η", + "7945": "I", + "7946": "P", + "7947": "N", + "7948": "ó", + "7949": "λ", + "7950": "x", + "7951": "ж", + "7952": "й", + "7953": "å", + "7954": "ί", + "7955": "ό", + "7956": "?", + "7957": "ά", + "7958": "х", + "7959": "J", + "7960": "C", + "7961": "ы", + "7962": "έ", + "7963": "B", + "7964": "H", + "7965": "ü", + "7966": "K", + "7967": "ш", + "7968": "L", + "7969": "V", + "7970": "O", + "7971": "ą", + "7972": "ē", + "7973": "ц", + "7974": "ż", + "7975": "ī", + "7976": "ю", + "7977": "γ", + "7978": "щ", + "7979": "ъ", + "7980": "ł", + "7981": "δ", + "7982": "ý", + "7983": "ę", + "7984": "ė", + "7985": "ě", + "7986": "ω", + "7987": "U", + "7988": "ή", + "7989": "ú", + "7990": "G", + "7991": "R", + "7992": "W", + "7993": "ș", + "7994": "χ", + "7995": "F", + "7996": "õ", + "7997": "ć", + "7998": "ț", + "7999": "θ", + "8000": "ų", + "8001": "î", + "8002": "ύ", + "8003": "ū", + "8004": "ř", + "8005": "ġ", + "8006": "ã", + "8007": "à", + "8008": "ś", + "8009": "ő", + "8010": "ť", + "8011": "æ", + "8012": "ώ", + "8013": "э", + "8014": "φ", + "8015": "ø", + "8016": "ņ", + "8017": "è", + "8018": "є", + "8019": "Z", + "8020": "!", + "8021": "į", + "8022": "ф", + "8023": "β", + "8024": "â", + "8025": "ç", + "8026": "Т", + "8027": "ê", + "8028": "ċ", + "8029": "Н", + "8030": "ї", + "8031": "В", + "8032": "ξ", + "8033": ":", + "8034": "С", + "8035": "ů", + "8036": "ζ", + "8037": "П", + "8038": "ľ", + "8039": "Д", + "8040": "Y", + "8041": "К", + "8042": "М", + "8043": "О", + "8044": "ļ", + "8045": "ô", + "8046": "И", + "8047": "Q", + "8048": "А", + "8049": "Я", + "8050": "Α", + "8051": "Б", + "8052": "Ε", + "8053": "ď", + "8054": "ß", + "8055": "З", + "8056": "Τ", + "8057": "Š", + "8058": "Κ", + "8059": "đ", + "8060": "Ο", + "8061": "ű", + "8062": "Е", + "8063": "ň", + "8064": "Σ", + "8065": "Π", + "8066": "ñ", + "8067": "ù", + "8068": "Μ", + "8069": "У", + "8070": "É", + "8071": "ψ", + "8072": "Р", + "8073": "ń", + "8074": "Г", + "8075": "ò", + "8076": "ì", + "8077": "Č", + "8078": "Ц", + "8079": "Л", + "8080": "¿", + "8081": "Э", + "8082": "Х", + "8083": "Ș", + "8084": "ķ", + "8085": "Η", + "8086": "Έ", + "8087": "Î", + "8088": "І", + "8089": "Ч", + "8090": "Ν", + "8091": "X", + "8092": "ģ", + "8093": "Δ", + "8094": "Γ", + "8095": "Ž", + "8096": "Ф", + "8097": "ź", + "8098": "¡", + "8099": "Á", + "8100": "È", + "8101": "ë", + "8102": "Θ", + "8103": "Ό", + "8104": "Ω", + "8105": "Щ", + "8106": "Ú", + "8107": "Ш", + "8108": "Λ", + "8109": "Ü", + "8110": "Å", + "8111": "Ä", + "8112": "Ι", + "8113": "û", + "8114": "ϊ", + "8115": "Χ", + "8116": "Β", + "8117": "Є", + "8118": "Φ", + "8119": "Ρ", + "8120": "Й", + "8121": "Ħ", + "8122": "Ġ", + "8123": "Ή", + "8124": "Ó", + "8125": "Ö", + "8126": "Υ", + "8127": "Í", + "8128": "Ά", + "8129": "ï", + "8130": "œ", + "8131": "À", + "8132": "%", + "8133": "Ж", + "8134": "Ξ", + "8135": "Ю", + "8136": "Ř", + "8137": "Ő", + "8138": "Ď", + "8139": "Ї", + "8140": "Ċ", + "8141": "ŕ", + "8142": "Į", + "8143": "Ż", + "8144": "ĺ", + "8145": "Ś", + "8146": "Ā", + "8147": "/", + "8148": "Ņ", + "8149": "Ζ", + "8150": "ґ", + "8151": "Ç", + "8152": "Õ", + "8153": "Ķ", + "8154": "Ī", + "8155": "Ľ", + "8156": "Ļ", + "8157": "Ē", + "8158": "Ț", + "8159": "Ł", + "8160": "Æ", + "8161": "Ø", + "8162": "Ê", + "8163": "Ь", + "8164": "Ă", + "8165": "Ъ", + "8166": "Ū", + "8167": "Ψ", + "8168": "Ģ", + "8169": "Ė", + "8170": "Ы", + "8171": "Ć", + "8172": "Ě", + "8173": "Ę", + "8174": "Ą", + "8175": "Ť", + "8176": "Ý", + "8177": "Ґ", + "8178": "Ô", + "8179": "$", + "8180": "Â", + "8181": "Ã", + "8182": "Ų", + "8183": "Ű", + "8184": "Ź", + "8185": "€", + "8186": "Ů", + "8187": "Ň", + "8188": "£", + "8189": "Ì", + "8190": "Ń", + "8191": "Œ" +} \ No newline at end of file