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Sortformer_balanced.mlmodelc/analytics/coremldata.bin

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Sortformer_balanced.mlmodelc/coremldata.bin

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Sortformer_balanced.mlmodelc/metadata.json

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+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
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+        "formattedType" : "MultiArray (Float32 1 × 349 × 4)",
+        "shortDescription" : "",
+        "shape" : "[1, 349, 4]",
+        "name" : "speaker_preds_out",
+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
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+        "shape" : "[1, 121, 512]",
+        "name" : "chunk_pre_encoder_embs_out",
+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "chunk_pre_encoder_lengths_out",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
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+      "Ios17.reshape" : 175,
+      "Ios16.softmax" : 35,
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+      "Ios17.transpose" : 193,
+      "Ios17.maximum" : 1,
+      "Split" : 17,
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+      "Select" : 51,
+      "Ios17.add" : 187,
+      "Tile" : 9,
+      "Ios17.gatherAlongAxis" : 1,
+      "Ios17.sliceByIndex" : 34,
+      "Ios16.sigmoid" : 18,
+      "Pad" : 34,
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+      "Ios17.layerNorm" : 121,
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+      "Ios17.sub" : 6,
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+      "Ios16.relu" : 23,
+      "Ios17.cast" : 24,
+      "Ios17.linear" : 248,
+      "Ios17.concat" : 1,
+      "Ios17.floorDiv" : 3,
+      "Ios17.minimum" : 1,
+      "Ios17.greaterEqual" : 2,
+      "Ios16.silu" : 51,
+      "Ios17.mul" : 119
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
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+    ],
+    "availability" : {
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+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
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+      "structure" : [
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+          "name" : "MLModelType_mlProgram"
+        },
+        {
+          "name" : "MLModelType_mlProgram"
+        }
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+    },
+    "userDefinedMetadata" : {
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+    "inputSchema" : [
+      {
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+        "isOptional" : "0",
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+        "formattedType" : "MultiArray (Float32 1 × 968 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 968, 128]",
+        "name" : "chunk",
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+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
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+        "type" : "MultiArray"
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+      {
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+        "dataType" : "Float32",
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+        "shortDescription" : "",
+        "shape" : "[1, 188, 512]",
+        "name" : "spkcache",
+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
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+        "type" : "MultiArray"
+      },
+      {
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+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 40 × 512)",
+        "shortDescription" : "",
+        "shape" : "[1, 40, 512]",
+        "name" : "fifo",
+        "type" : "MultiArray"
+      },
+      {
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+        "dataType" : "Int32",
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+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "fifo_lengths",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "Sortformer",
+    "method" : "predict"
+  }
+]

Sortformer_balanced.mlmodelc/model0/analytics/coremldata.bin

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Sortformer_balanced.mlmodelc/model0/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
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Sortformer_balanced.mlmodelc/model0/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 968, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<string, []> chunk_to_fp16_dtype_0 = const()[name = tensor<string, []>("chunk_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 968, 128]> chunk_to_fp16 = cast(dtype = chunk_to_fp16_dtype_0, x = chunk)[name = tensor<string, []>("cast_32")];
+            tensor<fp16, [1, 1, 968, 128]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = chunk_to_fp16)[name = tensor<string, []>("tensor_1_cast_fp16")];
+            tensor<int32, [1, 968]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 968]>([[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]])];
+            tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_40 = expand_dims(axes = var_40_axes_0, x = chunk_lengths)[name = tensor<string, []>("op_40")];
+            tensor<bool, [1, 968]> time_mask_1 = less(x = expand_dims_0, y = var_40)[name = tensor<string, []>("time_mask_1")];
+            tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 968, 1]> var_42 = expand_dims(axes = var_42_axes_0, x = time_mask_1)[name = tensor<string, []>("op_42")];
+            tensor<int32, [3]> var_44_reps_0 = const()[name = tensor<string, []>("op_44_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
+            tensor<bool, [1, 968, 128]> var_44 = tile(reps = var_44_reps_0, x = var_42)[name = tensor<string, []>("op_44")];
+            tensor<int32, [1]> var_50_axes_0 = const()[name = tensor<string, []>("op_50_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<string, []> cast_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 968, 128]> var_44_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = var_44)[name = tensor<string, []>("cast_31")];
+            tensor<fp16, [1, 1, 968, 128]> var_50_cast_fp16 = expand_dims(axes = var_50_axes_0, x = var_44_to_fp16)[name = tensor<string, []>("op_50_cast_fp16")];
+            tensor<fp16, [1, 1, 968, 128]> input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_50_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [256]> model_encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(4736)))];
+            tensor<fp16, [1, 256, 484, 64]> tensor_3_cast_fp16 = conv(bias = model_encoder_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = model_encoder_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("tensor_3_cast_fp16")];
+            tensor<string, []> cast_0_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_0_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_61_promoted_to_fp16 = const()[name = tensor<string, []>("op_61_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> chunk_lengths_to_fp16 = cast(dtype = cast_0_to_fp16_dtype_0, x = chunk_lengths)[name = tensor<string, []>("cast_30")];
+            tensor<fp16, [1]> var_62_cast_fp16 = add(x = chunk_lengths_to_fp16, y = var_61_promoted_to_fp16)[name = tensor<string, []>("op_62_cast_fp16")];
+            tensor<fp16, []> var_63_promoted_to_fp16 = const()[name = tensor<string, []>("op_63_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> var_64_cast_fp16 = add(x = var_62_cast_fp16, y = var_63_promoted_to_fp16)[name = tensor<string, []>("op_64_cast_fp16")];
+            tensor<fp16, []> var_65_promoted_to_fp16 = const()[name = tensor<string, []>("op_65_promoted_to_fp16"), val = tensor<fp16, []>(0x1.8p+1)];
+            tensor<fp16, [1]> var_66_cast_fp16 = sub(x = var_64_cast_fp16, y = var_65_promoted_to_fp16)[name = tensor<string, []>("op_66_cast_fp16")];
+            tensor<fp16, []> var_21_promoted_to_fp16 = const()[name = tensor<string, []>("op_21_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1]> floor_div_0_cast_fp16 = floor_div(x = var_66_cast_fp16, y = var_21_promoted_to_fp16)[name = tensor<string, []>("floor_div_0_cast_fp16")];
+            tensor<fp16, []> var_68_promoted_to_fp16 = const()[name = tensor<string, []>("op_68_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_68_promoted_to_fp16)[name = tensor<string, []>("current_lengths_3_cast_fp16")];
+            tensor<string, []> cast_3_dtype_0 = const()[name = tensor<string, []>("cast_3_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 484]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 484]>([[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]])];
+            tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = cast_3_dtype_0, x = current_lengths_3_cast_fp16)[name = tensor<string, []>("cast_29")];
+            tensor<int32, [1, 1]> var_77 = expand_dims(axes = var_77_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = tensor<string, []>("op_77")];
+            tensor<bool, [1, 484]> time_mask_3 = less(x = expand_dims_1, y = var_77)[name = tensor<string, []>("time_mask_3")];
+            tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 484, 1]> var_79 = expand_dims(axes = var_79_axes_0, x = time_mask_3)[name = tensor<string, []>("op_79")];
+            tensor<int32, [3]> var_81_reps_0 = const()[name = tensor<string, []>("op_81_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
+            tensor<bool, [1, 484, 64]> var_81 = tile(reps = var_81_reps_0, x = var_79)[name = tensor<string, []>("op_81")];
+            tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<string, []> cast_4_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_4_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 484, 64]> var_81_to_fp16 = cast(dtype = cast_4_to_fp16_dtype_0, x = var_81)[name = tensor<string, []>("cast_28")];
+            tensor<fp16, [1, 1, 484, 64]> var_87_cast_fp16 = expand_dims(axes = var_87_axes_0, x = var_81_to_fp16)[name = tensor<string, []>("op_87_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = tensor<string, []>("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 484, 64]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_87_cast_fp16)[name = tensor<string, []>("expanded_mask_3_cast_fp16")];
+            tensor<fp16, [1, 256, 484, 64]> input_3_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [1, 256, 484, 64]> tensor_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor<string, []>("tensor_5_cast_fp16")];
+            tensor<fp16, [1, 256, 484, 64]> input_5_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(5312)))];
+            tensor<fp16, [256]> model_encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(9984)))];
+            tensor<fp16, [1, 256, 242, 32]> tensor_7_cast_fp16 = conv(bias = model_encoder_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = model_encoder_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("tensor_7_cast_fp16")];
+            tensor<fp16, []> var_107_promoted_to_fp16 = const()[name = tensor<string, []>("op_107_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> var_108_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_107_promoted_to_fp16)[name = tensor<string, []>("op_108_cast_fp16")];
+            tensor<fp16, []> var_109_promoted_to_fp16 = const()[name = tensor<string, []>("op_109_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> var_110_cast_fp16 = add(x = var_108_cast_fp16, y = var_109_promoted_to_fp16)[name = tensor<string, []>("op_110_cast_fp16")];
+            tensor<fp16, []> var_111_promoted_to_fp16 = const()[name = tensor<string, []>("op_111_promoted_to_fp16"), val = tensor<fp16, []>(0x1.8p+1)];
+            tensor<fp16, [1]> var_112_cast_fp16 = sub(x = var_110_cast_fp16, y = var_111_promoted_to_fp16)[name = tensor<string, []>("op_112_cast_fp16")];
+            tensor<fp16, []> var_21_promoted_1_to_fp16 = const()[name = tensor<string, []>("op_21_promoted_1_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_112_cast_fp16, y = var_21_promoted_1_to_fp16)[name = tensor<string, []>("floor_div_1_cast_fp16")];
+            tensor<fp16, []> var_114_promoted_to_fp16 = const()[name = tensor<string, []>("op_114_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_114_promoted_to_fp16)[name = tensor<string, []>("current_lengths_5_cast_fp16")];
+            tensor<string, []> cast_5_dtype_0 = const()[name = tensor<string, []>("cast_5_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 242]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 242]>([[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]])];
+            tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = cast_5_dtype_0, x = current_lengths_5_cast_fp16)[name = tensor<string, []>("cast_27")];
+            tensor<int32, [1, 1]> var_123 = expand_dims(axes = var_123_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = tensor<string, []>("op_123")];
+            tensor<bool, [1, 242]> time_mask_5 = less(x = expand_dims_2, y = var_123)[name = tensor<string, []>("time_mask_5")];
+            tensor<int32, [1]> var_125_axes_0 = const()[name = tensor<string, []>("op_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 242, 1]> var_125 = expand_dims(axes = var_125_axes_0, x = time_mask_5)[name = tensor<string, []>("op_125")];
+            tensor<int32, [3]> var_127_reps_0 = const()[name = tensor<string, []>("op_127_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
+            tensor<bool, [1, 242, 32]> var_127 = tile(reps = var_127_reps_0, x = var_125)[name = tensor<string, []>("op_127")];
+            tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<string, []> cast_6_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_6_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 242, 32]> var_127_to_fp16 = cast(dtype = cast_6_to_fp16_dtype_0, x = var_127)[name = tensor<string, []>("cast_26")];
+            tensor<fp16, [1, 1, 242, 32]> var_133_cast_fp16 = expand_dims(axes = var_133_axes_0, x = var_127_to_fp16)[name = tensor<string, []>("op_133_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = tensor<string, []>("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 242, 32]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_133_cast_fp16)[name = tensor<string, []>("expanded_mask_7_cast_fp16")];
+            tensor<fp16, [1, 256, 242, 32]> input_7_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
+            tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(10560)))];
+            tensor<fp16, [256]> model_encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(141696)))];
+            tensor<fp16, [1, 256, 242, 32]> tensor_9_cast_fp16 = conv(bias = model_encoder_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = model_encoder_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("tensor_9_cast_fp16")];
+            tensor<fp16, [1, 256, 242, 32]> input_9_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<fp16, [1, 256, 242, 32]> tensor_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("tensor_11_cast_fp16")];
+            tensor<fp16, [1, 256, 242, 32]> input_11_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
+            tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(142272)))];
+            tensor<fp16, [256]> model_encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(146944)))];
+            tensor<fp16, [1, 256, 121, 16]> tensor_13_cast_fp16 = conv(bias = model_encoder_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = model_encoder_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("tensor_13_cast_fp16")];
+            tensor<fp16, []> var_168_promoted_to_fp16 = const()[name = tensor<string, []>("op_168_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> var_169_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_168_promoted_to_fp16)[name = tensor<string, []>("op_169_cast_fp16")];
+            tensor<fp16, []> var_170_promoted_to_fp16 = const()[name = tensor<string, []>("op_170_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> var_171_cast_fp16 = add(x = var_169_cast_fp16, y = var_170_promoted_to_fp16)[name = tensor<string, []>("op_171_cast_fp16")];
+            tensor<fp16, []> var_172_promoted_to_fp16 = const()[name = tensor<string, []>("op_172_promoted_to_fp16"), val = tensor<fp16, []>(0x1.8p+1)];
+            tensor<fp16, [1]> var_173_cast_fp16 = sub(x = var_171_cast_fp16, y = var_172_promoted_to_fp16)[name = tensor<string, []>("op_173_cast_fp16")];
+            tensor<fp16, []> var_21_promoted_2_to_fp16 = const()[name = tensor<string, []>("op_21_promoted_2_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_173_cast_fp16, y = var_21_promoted_2_to_fp16)[name = tensor<string, []>("floor_div_2_cast_fp16")];
+            tensor<fp16, []> var_175_promoted_to_fp16 = const()[name = tensor<string, []>("op_175_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_175_promoted_to_fp16)[name = tensor<string, []>("current_lengths_cast_fp16")];
+            tensor<string, []> cast_7_dtype_0 = const()[name = tensor<string, []>("cast_7_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 121]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 121]>([[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]])];
+            tensor<int32, [1]> var_184_axes_0 = const()[name = tensor<string, []>("op_184_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = cast_7_dtype_0, x = current_lengths_cast_fp16)[name = tensor<string, []>("cast_25")];
+            tensor<int32, [1, 1]> var_184 = expand_dims(axes = var_184_axes_0, x = current_lengths_cast_fp16_to_int32)[name = tensor<string, []>("op_184")];
+            tensor<bool, [1, 121]> time_mask = less(x = expand_dims_3, y = var_184)[name = tensor<string, []>("time_mask")];
+            tensor<int32, [1]> var_186_axes_0 = const()[name = tensor<string, []>("op_186_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 121, 1]> var_186 = expand_dims(axes = var_186_axes_0, x = time_mask)[name = tensor<string, []>("op_186")];
+            tensor<int32, [3]> var_188_reps_0 = const()[name = tensor<string, []>("op_188_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
+            tensor<bool, [1, 121, 16]> var_188 = tile(reps = var_188_reps_0, x = var_186)[name = tensor<string, []>("op_188")];
+            tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<string, []> cast_8_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_8_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 121, 16]> var_188_to_fp16 = cast(dtype = cast_8_to_fp16_dtype_0, x = var_188)[name = tensor<string, []>("cast_24")];
+            tensor<fp16, [1, 1, 121, 16]> var_194_cast_fp16 = expand_dims(axes = var_194_axes_0, x = var_188_to_fp16)[name = tensor<string, []>("op_194_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = tensor<string, []>("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 121, 16]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_194_cast_fp16)[name = tensor<string, []>("expanded_mask_13_cast_fp16")];
+            tensor<fp16, [1, 256, 121, 16]> input_13_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
+            tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(147520)))];
+            tensor<fp16, [256]> model_encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(278656)))];
+            tensor<fp16, [1, 256, 121, 16]> tensor_15_cast_fp16 = conv(bias = model_encoder_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = model_encoder_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("tensor_15_cast_fp16")];
+            tensor<fp16, [1, 256, 121, 16]> input_15_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp16, [1, 256, 121, 16]> tensor_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor<string, []>("tensor_cast_fp16")];
+            tensor<fp16, [1, 256, 121, 16]> x_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
+            tensor<int32, [4]> var_228_perm_0 = const()[name = tensor<string, []>("op_228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 121, -1])];
+            tensor<fp16, [1, 121, 256, 16]> var_228_cast_fp16 = transpose(perm = var_228_perm_0, x = x_cast_fp16)[name = tensor<string, []>("transpose_0")];
+            tensor<fp16, [1, 121, 4096]> input_cast_fp16 = reshape(shape = var_229, x = var_228_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<fp16, [512, 4096]> model_encoder_pre_encode_out_weight_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight_to_fp16"), val = tensor<fp16, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(279232)))];
+            tensor<fp16, [512]> model_encoder_pre_encode_out_bias_to_fp16 = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(4473600)))];
+            tensor<fp16, [1, 121, 512]> linear_0_cast_fp16 = linear(bias = model_encoder_pre_encode_out_bias_to_fp16, weight = model_encoder_pre_encode_out_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<string, []> linear_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_0_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<string, []> cast_11_dtype_0 = const()[name = tensor<string, []>("cast_11_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1]> cap0 = const()[name = tensor<string, []>("cap0"), val = tensor<int32, [1]>([188])];
+            tensor<int32, [1]> cap1 = const()[name = tensor<string, []>("cap1"), val = tensor<int32, [1]>([40])];
+            tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
+            tensor<bool, []> full_interleave_0 = const()[name = tensor<string, []>("full_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> spkcache_to_fp16_dtype_0 = const()[name = tensor<string, []>("spkcache_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<string, []> fifo_to_fp16_dtype_0 = const()[name = tensor<string, []>("fifo_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 40, 512]> fifo_to_fp16 = cast(dtype = fifo_to_fp16_dtype_0, x = fifo)[name = tensor<string, []>("cast_20")];
+            tensor<fp16, [1, 188, 512]> spkcache_to_fp16 = cast(dtype = spkcache_to_fp16_dtype_0, x = spkcache)[name = tensor<string, []>("cast_21")];
+            tensor<fp16, [1, 349, 512]> full_cast_fp16 = concat(axis = var_264, interleave = full_interleave_0, values = (spkcache_to_fp16, fifo_to_fp16, linear_0_cast_fp16))[name = tensor<string, []>("full_cast_fp16")];
+            tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
+            tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = cast_11_dtype_0, x = current_lengths_cast_fp16)[name = tensor<string, []>("cast_22")];
+            tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
+            tensor<int32, [349]> positions = const()[name = tensor<string, []>("positions"), val = tensor<int32, [349]>([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])];
+            tensor<bool, [349]> var_284 = greater_equal(x = positions, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
+            tensor<string, []> cast_12_dtype_0 = const()[name = tensor<string, []>("cast_12_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<bool, [349]> var_290 = greater_equal(x = positions, y = var_273)[name = tensor<string, []>("op_290")];
+            tensor<string, []> cast_13_dtype_0 = const()[name = tensor<string, []>("cast_13_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1]> var_297 = sub(x = cap0, y = spkcache_lengths)[name = tensor<string, []>("op_297")];
+            tensor<int32, [349]> cast_12 = cast(dtype = cast_12_dtype_0, x = var_284)[name = tensor<string, []>("cast_19")];
+            tensor<int32, [349]> var_298 = mul(x = cast_12, y = var_297)[name = tensor<string, []>("op_298")];
+            tensor<int32, [1]> var_300 = sub(x = cap1, y = fifo_lengths)[name = tensor<string, []>("op_300")];
+            tensor<int32, [349]> cast_13 = cast(dtype = cast_13_dtype_0, x = var_290)[name = tensor<string, []>("cast_18")];
+            tensor<int32, [349]> var_301 = mul(x = cast_13, y = var_300)[name = tensor<string, []>("op_301")];
+            tensor<int32, [349]> offset = add(x = var_298, y = var_301)[name = tensor<string, []>("offset")];
+            tensor<int32, [349]> var_305 = add(x = positions, y = offset)[name = tensor<string, []>("op_305")];
+            tensor<int32, []> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, []>(348)];
+            tensor<int32, []> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, []>(0)];
+            tensor<int32, [349]> minimum_0 = minimum(x = var_305, y = var_309)[name = tensor<string, []>("minimum_0")];
+            tensor<int32, [349]> maximum_0 = maximum(x = minimum_0, y = var_310)[name = tensor<string, []>("maximum_0")];
+            tensor<int32, [1]> var_313_axes_0 = const()[name = tensor<string, []>("op_313_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, 349]> var_313 = expand_dims(axes = var_313_axes_0, x = maximum_0)[name = tensor<string, []>("op_313")];
+            tensor<int32, [1]> var_315_axes_0 = const()[name = tensor<string, []>("op_315_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1, 349, 1]> var_315 = expand_dims(axes = var_315_axes_0, x = var_313)[name = tensor<string, []>("op_315")];
+            tensor<int32, [3]> gather_idx_reps_0 = const()[name = tensor<string, []>("gather_idx_reps_0"), val = tensor<int32, [3]>([1, 1, 512])];
+            tensor<int32, [1, 349, 512]> gather_idx = tile(reps = gather_idx_reps_0, x = var_315)[name = tensor<string, []>("gather_idx")];
+            tensor<int32, []> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, []>(1)];
+            tensor<bool, []> packed_validate_indices_0 = const()[name = tensor<string, []>("packed_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> gather_idx_to_int16_dtype_0 = const()[name = tensor<string, []>("gather_idx_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<int16, [1, 349, 512]> gather_idx_to_int16 = cast(dtype = gather_idx_to_int16_dtype_0, x = gather_idx)[name = tensor<string, []>("cast_17")];
+            tensor<fp16, [1, 349, 512]> packed_cast_fp16_cast_uint16 = gather_along_axis(axis = var_320, indices = gather_idx_to_int16, validate_indices = packed_validate_indices_0, x = full_cast_fp16)[name = tensor<string, []>("packed_cast_fp16_cast_uint16")];
+            tensor<bool, [349]> var_323 = less(x = positions, y = pre_encoder_lengths)[name = tensor<string, []>("op_323")];
+            tensor<int32, [1]> var_330_axes_0 = const()[name = tensor<string, []>("op_330_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<string, []> cast_14_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_14_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [349]> var_323_to_fp16 = cast(dtype = cast_14_to_fp16_dtype_0, x = var_323)[name = tensor<string, []>("cast_16")];
+            tensor<fp16, [1, 349]> var_330_cast_fp16 = expand_dims(axes = var_330_axes_0, x = var_323_to_fp16)[name = tensor<string, []>("op_330_cast_fp16")];
+            tensor<int32, [1]> valid_mask_axes_0 = const()[name = tensor<string, []>("valid_mask_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 349, 1]> valid_mask_cast_fp16 = expand_dims(axes = valid_mask_axes_0, x = var_330_cast_fp16)[name = tensor<string, []>("valid_mask_cast_fp16")];
+            tensor<fp16, [1, 349, 512]> var_333_cast_fp16 = mul(x = packed_cast_fp16_cast_uint16, y = valid_mask_cast_fp16)[name = tensor<string, []>("op_333_cast_fp16")];
+            tensor<string, []> var_333_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_333_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [1, 349, 512]> pre_encoder_embs = cast(dtype = var_333_cast_fp16_to_fp32_dtype_0, x = var_333_cast_fp16)[name = tensor<string, []>("cast_15")];
+            tensor<fp32, [1, 121, 512]> chunk_pre_encoder_embs = cast(dtype = linear_0_cast_fp16_to_fp32_dtype_0, x = linear_0_cast_fp16)[name = tensor<string, []>("cast_23")];
+        } -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
+}

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