Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
stringlengths
24
117
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
emu3/modeling_emu3.py:Emu3VQVAEEncoderConvDownsample
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[ "Conv2d", "F", "ModelVQVAEEncoderConvDownsample", "Module", "__init__", "class", "constant", "conv", "def", "forward", "hidden_states", "in_channels", "kernel_size", "mode", "nn", "pad", "padding", "return", "self", "stride", "super", "value" ]
emu3/modeling_emu3.py:Emu3VQVAEEncoderConvUpsample
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[ "Conv2d", "F", "ModelVQVAEEncoderConvUpsample", "Module", "__init__", "class", "conv", "def", "forward", "hidden_states", "in_channels", "interpolate", "kernel_size", "mode", "nearest", "nn", "padding", "return", "scale_factor", "self", "stride", "super" ]
emu3/modeling_emu3.py:Emu3VQVAEConv3d
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[ "Conv3d", "F", "ModelVQVAEConv3d", "Module", "__init__", "class", "conv", "def", "for", "forward", "hidden_states", "in", "in_channel", "kernel_size", "nn", "one_kernel", "one_stride", "out_channel", "pad", "pad_size", "padding", "padding_sizes", "return", "self", "st...
emu3/modeling_emu3.py:Emu3VQVAESpatialNorm
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[ "Conv2d", "F", "GroupNorm", "ModelVQVAESpatialNorm", "Module", "True", "__init__", "affine", "class", "conv_b", "conv_y", "def", "eps", "forward", "hidden_states", "in_channels", "interpolate", "kernel_size", "mode", "nearest", "nn", "norm_layer", "num_channels", "num_g...
emu3/modeling_emu3.py:Emu3VQVAETemporalUpsample
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[ "F", "ModelVQVAEConv3d", "ModelVQVAETemporalUpsample", "Module", "__init__", "batch_size", "channels", "class", "contiguous", "conv", "def", "forward", "height", "hidden_states", "in_channel", "interpolate", "kernel_size", "mode", "nearest", "nn", "out_channel", "permute", ...
emu3/modeling_emu3.py:Emu3VQVAETemporalDownsample
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[ "ModelVQVAEConv3d", "ModelVQVAETemporalDownsample", "Module", "__init__", "class", "conv", "def", "forward", "hidden_states", "in_channel", "kernel_size", "nn", "out_channel", "return", "self", "stride", "super" ]
emu3/modeling_emu3.py:Emu3VQVAETemporalResnetBlock
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[ "BatchNorm3d", "Conv3d", "ModelVQVAEConv3d", "ModelVQVAETemporalResnetBlock", "Module", "None", "__init__", "class", "conv1", "conv2", "def", "else", "forward", "hidden_states", "if", "in_channels", "is", "kernel_size", "nin_shortcut", "nn", "norm1", "norm2", "out_channel...
emu3/modeling_emu3.py:Emu3VQVAEResnetBlock
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[ "Conv2d", "GroupNorm", "ModelVQVAEResnetBlock", "ModelVQVAESpatialNorm", "Module", "None", "True", "__init__", "affine", "class", "conv1", "conv2", "def", "else", "eps", "forward", "hidden_states", "if", "in_channels", "is", "kernel_size", "nin_shortcut", "nn", "norm1",...
emu3/modeling_emu3.py:Emu3VQVAEAttentionBlock
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelVQVAEAttentionBlock", "Module", "None", "ValueError", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "batch_size", "be", "by", "class", "...
emu3/modeling_emu3.py:Emu3VQVAEGroupNorm
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[ "F", "GroupNorm", "ModelVQVAEGroupNorm", "None", "__init__", "bias", "class", "def", "eps", "forward", "group_norm", "input", "kwargs", "nn", "num_groups", "quant_states", "return", "self", "super", "weight" ]
emu3/modeling_emu3.py:Emu3VQVAEMiddleBlock
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[ "ModelVQVAEAttentionBlock", "ModelVQVAEGroupNorm", "ModelVQVAEMiddleBlock", "ModelVQVAEResnetBlock", "ModelVQVAESpatialNorm", "Module", "None", "True", "__init__", "affine", "attn_1", "attn_norm", "batch_size", "block_1", "block_2", "channels", "class", "config", "def", "else",...
emu3/modeling_emu3.py:Emu3VQVAEDownBlock
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[ "GroupNorm", "ModelVQVAEDownBlock", "ModelVQVAEEncoderConvDownsample", "ModelVQVAEResnetBlock", "Module", "ModuleList", "None", "True", "__init__", "affine", "and", "append", "attn", "attn_norms", "attn_resolutions", "base_channels", "batch_size", "block", "block_in", "block_ou...
emu3/modeling_emu3.py:Emu3VQVAEUpBlock
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[ "ModelVQVAEEncoderConvUpsample", "ModelVQVAEResnetBlock", "ModelVQVAESpatialNorm", "ModelVQVAEUpBlock", "Module", "ModuleList", "__init__", "append", "attn", "attn_norms", "attn_resolutions", "base_channels", "batch_size", "block", "block_in", "block_out", "blocks", "channel_multip...
emu3/modeling_emu3.py:Emu3VQVAEEncoder
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[ "Conv2d", "GroupNorm", "ModelVQVAEDownBlock", "ModelVQVAEEncoder", "ModelVQVAEMiddleBlock", "ModelVQVAETemporalDownsample", "ModelVQVAETemporalResnetBlock", "Module", "ModuleList", "True", "_", "__init__", "affine", "append", "base_channels", "block_in", "channel_multiplier", "clas...
emu3/modeling_emu3.py:Emu3VQVAEDecoder
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[ "Conv2d", "ModelVQVAEDecoder", "ModelVQVAEMiddleBlock", "ModelVQVAESpatialNorm", "ModelVQVAETemporalResnetBlock", "ModelVQVAETemporalUpsample", "ModelVQVAEUpBlock", "Module", "ModuleList", "_", "__init__", "append", "base_channels", "block_in", "cat", "channel_multiplier", "chunk", ...
emu3/modeling_emu3.py:Emu3VQVAE
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[ "BatchNorm2d", "BatchNorm3d", "Conv2d", "Conv3d", "Embedding", "False", "GroupNorm", "Linear", "ModelVQVAE", "ModelVQVAEAttentionBlock", "ModelVQVAEConfig", "ModelVQVAEConv3d", "ModelVQVAEDecoder", "ModelVQVAEEncoder", "ModelVQVAEModelOutput", "ModelVQVAEResnetBlock", "ModelVQVAETemp...
emu3/modeling_emu3.py:Emu3ImageVocabularyMapping
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[ "ModelImageVocabularyMapping", "bpe2img", "bpe2img_mapping_tensor", "cached_property", "cat", "class", "convert_bpe2img", "convert_img2bpe", "cpu", "def", "device", "dim", "dtype", "eol_row", "eol_token_id", "extra_200", "for", "get", "if", "image", "image_token_id", "image...
emu3/modeling_emu3.py:Emu3PreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
emu3/modeling_emu3.py:Emu3RotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "device", ...
emu3/modeling_emu3.py:Emu3TextModel
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModelTextModel", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cac...
emu3/modeling_emu3.py:Emu3ForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelPreTrainedModel", "ModelTextConfig", "ModelTextModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tu...
emu3/modeling_emu3.py:Emu3Model
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[ "Image", "ModelImageVocabularyMapping", "ModelModel", "ModelPreTrainedModel", "ModelTextModel", "ModelVQVAE", "None", "Tokenizes", "True", "VQGAN", "ValueError", "You", "__init__", "_checkpoint_conversion_mapping", "_from_config", "all", "and", "at", "attention_mask", "auto_doc...
emu3/modeling_emu3.py:Emu3ForConditionalGeneration
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[ "CausalLMOutputWithPast", "False", "GenerationMixin", "Linear", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_checkpoint_conversion_mapping", "_tied_weights_keys", "and", "attention_mask", "attentions", "auto_docstring", "cache_p...
deepseek_vl/modeling_deepseek_vl.py:DeepseekVLBaseModelOutputWithPast
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[ "ModelBaseModelOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "last_hidden_state", "past_key_values", "r" ]
deepseek_vl/modeling_deepseek_vl.py:DeepseekVLCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r" ]
deepseek_vl/modeling_deepseek_vl.py:DeepseekVLAligner
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[ "GELU", "Linear", "ModelAligner", "Module", "__init__", "activation", "class", "config", "def", "forward", "hidden_size", "in_features", "linear1", "linear2", "nn", "out_features", "return", "self", "super", "text_config", "vision_config", "vision_encodings", "x" ]
deepseek_vl/modeling_deepseek_vl.py:DeepseekVLPreTrainedModel
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[ "LlamaDecoderLayer", "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_no_split_modules", "_skip_keys_device_placement", "_supports_flash_attn", "_supports_sdpa", "base_model_prefix", "causal_mask", "class", "config", "image", "input_modaliti...
deepseek_vl/modeling_deepseek_vl.py:DeepseekVLModel
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[ "AutoModel", "False", "Image", "ModelAligner", "ModelBaseModelOutputWithPast", "ModelModel", "ModelPreTrainedModel", "None", "True", "ValueError", "You", "__init__", "aligner", "all", "and", "at", "attention_mask", "attentions", "auto_docstring", "both", "cache_position", "...
deepseek_vl/modeling_deepseek_vl.py:DeepseekVLForConditionalGeneration
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[ "False", "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_can_compile_fullgraph", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache_position", ...
instructblip/modeling_instructblip.py:BaseModelOutputWithVisionQformerOutputs
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[ "ModelModelOutputWithPooling", "ModelModelOutputWithVisionQformerOutputs", "None", "class", "qformer_outputs", "r", "vision_outputs" ]
instructblip/modeling_instructblip.py:InstructBlipForConditionalGenerationModelOutput
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instructblip/modeling_instructblip.py:InstructBlipVisionEmbeddings
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[ "Conv2d", "False", "ModelVisionEmbeddings", "Module", "Parameter", "_", "__init__", "align_corners", "and", "batch_size", "bicubic", "cat", "class", "class_embedding", "class_embeds", "class_pos_embed", "config", "def", "dim", "dtype", "else", "embed_dim", "embeddings", ...
instructblip/modeling_instructblip.py:eager_attention_forward
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instructblip/modeling_instructblip.py:InstructBlipAttention
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instructblip/modeling_instructblip.py:InstructBlipMLP
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instructblip/modeling_instructblip.py:InstructBlipEncoderLayer
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instructblip/modeling_instructblip.py:InstructBlipPreTrainedModel
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instructblip/modeling_instructblip.py:InstructBlipEncoder
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instructblip/modeling_instructblip.py:InstructBlipVisionModel
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instructblip/modeling_instructblip.py:InstructBlipQFormerMultiHeadAttention
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instructblip/modeling_instructblip.py:InstructBlipQFormerSelfOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelQFormerSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
instructblip/modeling_instructblip.py:InstructBlipQFormerAttention
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instructblip/modeling_instructblip.py:InstructBlipQFormerIntermediate
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instructblip/modeling_instructblip.py:InstructBlipQFormerOutput
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instructblip/modeling_instructblip.py:InstructBlipQFormerLayer
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[ "False", "GradientCheckpointingLayer", "ModelQFormerAttention", "ModelQFormerIntermediate", "ModelQFormerLayer", "ModelQFormerOutput", "None", "True", "__init__", "apply_chunking_to_forward", "attention", "attention_mask", "attention_output", "cat", "chunk_size_feed_forward", "class", ...
instructblip/modeling_instructblip.py:InstructBlipQFormerEncoder
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instructblip/modeling_instructblip.py:InstructBlipQFormerEmbeddings
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instructblip/modeling_instructblip.py:InstructBlipQFormerModel
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[ "BaseModelOutputWithPoolingAndCrossAttentions", "False", "ModelPreTrainedModel", "ModelQFormerEmbeddings", "ModelQFormerEncoder", "ModelQFormerLayer", "ModelQFormerModel", "ModelQFormerMultiHeadAttention", "None", "OutputRecorder", "ValueError", "Wrong", "_", "__init__", "_can_record_out...
instructblip/modeling_instructblip.py:InstructBlipModel
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[ "AutoModel", "False", "GPU", "Linear", "ModelForConditionalGenerationModelOutput", "ModelModel", "ModelPreTrainedModel", "ModelQFormerModel", "ModelVisionModel", "None", "Parameter", "Please", "The", "True", "__init__", "_hf_hook", "_keep_in_fp32_modules", "_preprocess_accelerate",...
instructblip/modeling_instructblip.py:InstructBlipForConditionalGeneration
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[ "AutoModelForCausalLM", "AutoModelForSeq2SeqLM", "BaseModelOutputWithVisionQformerOutputs", "False", "GPU", "GenerationMixin", "Linear", "ModelConfig", "ModelForConditionalGeneration", "ModelForConditionalGenerationModelOutput", "ModelPreTrainedModel", "ModelQFormerModel", "ModelVisionModel"...
phi/modeling_phi.py:PhiRotaryEmbedding
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phi/modeling_phi.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
phi/modeling_phi.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cos", "def", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
phi/modeling_phi.py:repeat_kv
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[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
phi/modeling_phi.py:eager_attention_forward
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phi/modeling_phi.py:PhiAttention
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[ "ALL_ATTENTION_FUNCTIONS", "LayerNorm", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache_po...
phi/modeling_phi.py:PhiMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
phi/modeling_phi.py:PhiDecoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "None", "Tensor", "__init__", "attention_mask", "attn_outputs", "cache_position", "class", "config", "def", "eps", "feed_forward_hidden_states", "forward", "hidden_...
phi/modeling_phi.py:PhiPreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
phi/modeling_phi.py:PhiModel
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[ "BaseModelOutputWithPast", "Dropout", "DynamicCache", "Embedding", "False", "LayerNorm", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRotaryEmbedding", "ModuleList", "None", "Setting", "True", "ValueError", "You", "__init__", "all_hidden_states", "all_self_att...
phi/modeling_phi.py:PhiForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
phi/modeling_phi.py:PhiForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
phi/modeling_phi.py:PhiForTokenClassification
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evolla/modeling_evolla.py:create_position_ids_from_input_ids
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evolla/modeling_evolla.py:EvollaSaProtEmbeddings
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evolla/modeling_evolla.py:rotate_half_esm
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[ "Model_half_esm", "cat", "chunk", "def", "dim", "return", "torch", "x", "x1", "x2" ]
evolla/modeling_evolla.py:apply_rotary_pos_emb_esm
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[ "Model_rotary_pos_emb_esm", "cos", "def", "return", "rotate_half_esm", "shape", "sin", "x" ]
evolla/modeling_evolla.py:EvollaSaProtRotaryEmbedding
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evolla/modeling_evolla.py:eager_attention_forward
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evolla/modeling_evolla.py:EvollaSaProtSelfAttention
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evolla/modeling_evolla.py:EvollaSaProtSelfOutput
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evolla/modeling_evolla.py:EvollaSaProtAttention
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evolla/modeling_evolla.py:gelu
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[ "Model", "def", "erf", "math", "return", "sqrt", "torch", "x" ]
evolla/modeling_evolla.py:EvollaSaProtIntermediate
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evolla/modeling_evolla.py:EvollaSaProtOutput
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evolla/modeling_evolla.py:EvollaSaProtLayer
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evolla/modeling_evolla.py:EvollaSaProtEncoder
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evolla/modeling_evolla.py:EvollaSaProtPooler
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evolla/modeling_evolla.py:EvollaSaProtPreTrainedModel
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evolla/modeling_evolla.py:EvollaSaProtProteinEncoder
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evolla/modeling_evolla.py:EvollaSequenceCompressorAttention
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evolla/modeling_evolla.py:EvollaFeedForward
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evolla/modeling_evolla.py:EvollaSequenceCompressorResampler
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evolla/modeling_evolla.py:EvollaProteinEncoderModelOutput
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[ "ModelOutput", "ModelProteinEncoderModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "sequence_compressor_output" ]
evolla/modeling_evolla.py:EvollaProteinEncoder
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evolla/modeling_evolla.py:EvollaSequenceAlignerCrossAttention
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evolla/modeling_evolla.py:EvollaRMSNorm
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evolla/modeling_evolla.py:EvollaRotaryEmbedding
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evolla/modeling_evolla.py:EvollaMLP
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evolla/modeling_evolla.py:rotate_half
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evolla/modeling_evolla.py:apply_rotary_pos_emb
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evolla/modeling_evolla.py:repeat_kv
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evolla/modeling_evolla.py:EvollaAttention
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evolla/modeling_evolla.py:EvollaDecoderLayer
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evolla/modeling_evolla.py:EvollaPreTrainedModel
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[ "False", "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelSequenceAlignerCrossAttention", "ModelSequenceCompressorResampler", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_skip_keys_dev...
evolla/modeling_evolla.py:EvollaModel
[ -0.00028458485030569136, 0.05579303950071335, 0.0048415446653962135, -0.012103862129151821, -0.001462550018914044, 0.04933764785528183, 0.02662849612534046, -0.010432376526296139, 0.0016066436655819416, 0.013544797897338867, 0.023054976016283035, 0.0015850295312702656, -0.0009798364480957389...
[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelProteinEncoder", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "au...
evolla/modeling_evolla.py:EvollaForProteinText2Text
[ -0.00023476459318771958, 0.06966105848550797, 0.016390837728977203, -0.017984390258789062, -0.0010528835700824857, 0.04803425818681717, 0.027887187898159027, -0.009504408575594425, -0.0026891217567026615, 0.034375227987766266, 0.049855463206768036, 0.011724001727998257, 0.0002507712633814662...
[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForProteinText2Text", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "def", "else", "forward", "get_input_embeddings", ...
prompt_depth_anything/modeling_prompt_depth_anything.py:PromptDepthAnythingLayer
[ 0.00008722027996554971, 0.0015386552549898624, 0.009503879584372044, -0.0018106036586686969, 0.0007478580228053033, 0.057481296360492706, 0.01843523606657982, -0.02862614393234253, 0.023931456729769707, -0.0072996667586266994, 0.03480939194560051, -0.038015518337488174, 0.0040076603181660175...
[ "Conv2d", "ModelLayer", "Model_depth", "Module", "ReLU", "__init__", "activation1", "activation2", "class", "config", "convolution1", "convolution2", "convolution3", "def", "forward", "fusion_hidden_size", "hidden_state", "kernel_size", "nn", "padding", "return", "self", ...
prompt_depth_anything/modeling_prompt_depth_anything.py:PromptDepthAnythingPreActResidualLayer
[ 0, 0.017853641882538795, 0.02892741933465004, -0.011808263137936592, 0.00046611562720499933, 0.04384312033653259, -0.008022839203476906, -0.021469568833708763, 0.013672725297510624, -0.014802702702581882, 0.02158256620168686, -0.03322133421897888, 0.0038984217680990696, -0.0126557452604174...
[ "Conv2d", "ModelPreActResidualLayer", "Module", "ReLU", "__init__", "activation1", "activation2", "class", "config", "convolution1", "convolution2", "def", "forward", "fusion_hidden_size", "hidden_state", "kernel_size", "nn", "padding", "residual", "return", "self", "stride...
prompt_depth_anything/modeling_prompt_depth_anything.py:PromptDepthAnythingFeatureFusionLayer
[ -0.0002046592126134783, 0.017276061698794365, 0.031390488147735596, -0.000988009967841208, -0.00038814678555354476, 0.04019789397716522, 0.022808916866779327, -0.05419940501451492, 0.0052505675703287125, -0.0038955823983997107, 0.006210348568856716, -0.011743204668164253, 0.00211716420017182...
[ "Conv2d", "False", "ModelFeatureFusionLayer", "ModelLayer", "ModelPreActResidualLayer", "Model_depth", "Model_depth_layer", "Module", "None", "True", "__init__", "align_corners", "bilinear", "class", "config", "def", "else", "forward", "functional", "fusion_hidden_size", "hid...