Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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 Community
1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
gpt_oss/modeling_gpt_oss.py:GptOssPreTrainedModel
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[ "False", "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelExperts", "ModelPreTrainedModel", "ModelTopKRouter", "OutputRecorder", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_keep_in_fp32_modules", "_no_split_modules", "_skip_...
gpt_oss/modeling_gpt_oss.py:GptOssModel
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[ "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "MoeModelOutputWithPast", "None", "ValueError", "You", "__init__", "_no_split_modules", "and", "arange", "attention_mask", "atten...
gpt_oss/modeling_gpt_oss.py:load_balancing_loss_func
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gpt_oss/modeling_gpt_oss.py:GptOssForCausalLM
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[ "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "MoeCausalLMOutputWithPast", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "aux_loss", "cache_position", "can_return_tuple", ...
gpt_oss/modeling_gpt_oss.py:GptOssForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
gpt_oss/modeling_gpt_oss.py:GptOssForTokenClassification
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[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
clipseg/modeling_clipseg.py:contrastive_loss
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[ "Model_loss", "arange", "cross_entropy", "def", "device", "functional", "len", "logits", "nn", "return", "torch" ]
clipseg/modeling_clipseg.py:clipseg_loss
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[ "Model_loss", "caption_loss", "contrastive_loss", "def", "image_loss", "return", "similarity", "t" ]
clipseg/modeling_clipseg.py:CLIPSegOutput
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[ "Model", "ModelOutput", "None", "class", "def", "else", "for", "getattr", "if", "image_embeds", "in", "k", "keys", "logits_per_image", "logits_per_text", "loss", "not", "r", "return", "self", "text_embeds", "text_model_output", "to_tuple", "tuple", "vision_model_outpu...
clipseg/modeling_clipseg.py:CLIPSegDecoderOutput
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[ "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "r" ]
clipseg/modeling_clipseg.py:CLIPSegImageSegmentationOutput
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[ "Model", "ModelOutput", "None", "class", "conditional_embeddings", "decoder_output", "def", "else", "for", "getattr", "if", "in", "k", "keys", "logits", "loss", "not", "pooled_output", "r", "return", "self", "to_tuple", "tuple", "vision_model_output" ]
clipseg/modeling_clipseg.py:CLIPSegVisionEmbeddings
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[ "Conv2d", "Embedding", "False", "Input", "Model", "Module", "Parameter", "True", "ValueError", "_", "__init__", "align_corners", "and", "arange", "batch_size", "bicubic", "cat", "class", "class_embedding", "class_embeds", "class_pos_embed", "config", "def", "dim", "do...
clipseg/modeling_clipseg.py:CLIPSegTextEmbeddings
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[ "Embedding", "False", "Model", "Module", "None", "Sequence", "ValueError", "__init__", "and", "arange", "be", "class", "config", "def", "else", "embed_dim", "embeddings", "expand", "f", "forward", "got", "hidden_size", "if", "input_ids", "inputs_embeds", "is", "le...
clipseg/modeling_clipseg.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "softmax", "...
clipseg/modeling_clipseg.py:CLIPSegAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "Model", "Module", "None", "ValueError", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "batch_size", "be", "by", "causal_attention_mask", "cla...
clipseg/modeling_clipseg.py:CLIPSegMLP
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[ "ACT2FN", "Linear", "Model", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
clipseg/modeling_clipseg.py:CLIPSegEncoderLayer
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[ "False", "GradientCheckpointingLayer", "LayerNorm", "Model", "ModelAttention", "ModelMLP", "__init__", "attention_mask", "attn_weights", "causal_attention_mask", "class", "config", "def", "embed_dim", "eps", "forward", "hidden_size", "hidden_states", "if", "layer_norm1", "lay...
clipseg/modeling_clipseg.py:CLIPSegPreTrainedModel
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[ "LayerNorm", "Linear", "Model", "ModelAttention", "ModelConfig", "ModelMLP", "ModelModel", "ModelTextEmbeddings", "ModelVisionEmbeddings", "None", "PreTrainedModel", "True", "_init_weights", "and", "arange", "base_model_prefix", "bias", "class", "class_embedding", "clip", "co...
clipseg/modeling_clipseg.py:CLIPSegEncoder
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[ "BaseModelOutput", "False", "Model", "ModelLayer", "Module", "ModuleList", "None", "_", "__init__", "all_attentions", "attention_mask", "attentions", "can_return_tuple", "causal_attention_mask", "class", "config", "def", "else", "encoder_layer", "encoder_states", "enumerate",...
clipseg/modeling_clipseg.py:CLIPSegTextTransformer
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[ "BaseModelOutputWithPooling", "LayerNorm", "Model", "ModelEncoder", "ModelTextEmbeddings", "Module", "None", "__init__", "_create_4d_causal_attention_mask", "_prepare_4d_attention_mask", "arange", "argmax", "attention_mask", "attentions", "auto_docstring", "causal_attention_mask", "c...
clipseg/modeling_clipseg.py:CLIPSegTextModel
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[ "Model", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelTextConfig", "ModelTextEmbeddings", "ModelTextTransformer", "None", "__init__", "_no_split_modules", "attention_mask", "auto_docstring", "class", "config", "def", "embeddings", "forward", "get_input_embeddings", "input_ids...
clipseg/modeling_clipseg.py:CLIPSegVisionTransformer
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[ "BaseModelOutputWithPooling", "LayerNorm", "Model", "ModelEncoder", "ModelVisionEmbeddings", "Module", "None", "True", "__init__", "attentions", "auto_docstring", "class", "config", "def", "else", "embed_dim", "embeddings", "encoder", "encoder_outputs", "eps", "forward", "h...
clipseg/modeling_clipseg.py:CLIPSegVisionModel
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[ "Model", "ModelPreTrainedModel", "ModelVisionConfig", "ModelVisionTransformer", "None", "True", "__init__", "auto_docstring", "class", "config", "def", "embeddings", "forward", "get_input_embeddings", "image", "input_modalities", "interpolate_pos_encoding", "kwargs", "main_input_...
clipseg/modeling_clipseg.py:CLIPSegModel
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[ "Linear", "Model", "ModelConfig", "ModelOutput", "ModelPreTrainedModel", "ModelTextConfig", "ModelTextTransformer", "ModelVisionConfig", "ModelVisionTransformer", "Model_loss", "None", "Parameter", "True", "TypeError", "__init__", "_attn_implementation", "attention_mask", "auto_doc...
clipseg/modeling_clipseg.py:CLIPSegDecoderLayer
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[ "False", "LayerNorm", "Model", "ModelAttention", "ModelMLP", "Module", "__init__", "attention_mask", "attn_weights", "causal_attention_mask", "class", "config", "def", "embed_dim", "eps", "forward", "hidden_size", "hidden_states", "if", "layer_norm1", "layer_norm2", "layer_...
clipseg/modeling_clipseg.py:CLIPSegDecoder
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[ "Conv2d", "ConvTranspose2d", "Linear", "Model", "ModelLayer", "ModelOutput", "ModelPreTrainedModel", "ModuleList", "None", "ReLU", "Sequential", "True", "_", "__init__", "activation", "activations", "all_attentions", "all_hidden_states", "attention_mask", "attentions", "batch...
clipseg/modeling_clipseg.py:CLIPSegForImageSegmentation
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[ "BCEWithLogitsLoss", "BaseModelOutputWithPooling", "Invalid", "Make", "Model", "ModelConfig", "ModelDecoder", "ModelImageSegmentationOutput", "ModelModel", "ModelPreTrainedModel", "None", "True", "ValueError", "__init__", "activations", "are", "as", "attention_mask", "attentions"...
gemma/modeling_gemma.py:GemmaRMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "_norm", "class", "def", "dim", "eps", "extra_repr", "f", "float", "forward", "keepdim", "mean", "nn", "output", "pow", "return", "rsqrt", "self", "shape", "super", "torch", "tuple", "type_as", "weight",...
gemma/modeling_gemma.py:GemmaMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "forward", "gate_proj", "hidden_act", "hidden_size", "intermediate_size", "nn", "return", "self", "super", "up_proj", "x" ]
gemma/modeling_gemma.py:GemmaRotaryEmbedding
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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", ...
gemma/modeling_gemma.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
gemma/modeling_gemma.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" ]
gemma/modeling_gemma.py:repeat_kv
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gemma/modeling_gemma.py:eager_attention_forward
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gemma/modeling_gemma.py:GemmaAttention
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gemma/modeling_gemma.py:GemmaDecoderLayer
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gemma/modeling_gemma.py:GemmaPreTrainedModel
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gemma/modeling_gemma.py:GemmaModel
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gemma/modeling_gemma.py:GemmaForCausalLM
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gemma/modeling_gemma.py:GemmaForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
gemma/modeling_gemma.py:GemmaForTokenClassification
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[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
patchtst/modeling_patchtst.py:eager_attention_forward
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patchtst/modeling_patchtst.py:PatchTSTAttention
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patchtst/modeling_patchtst.py:PatchTSTBatchNorm
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patchtst/modeling_patchtst.py:random_masking
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patchtst/modeling_patchtst.py:forecast_masking
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patchtst/modeling_patchtst.py:PatchTSTPatchify
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patchtst/modeling_patchtst.py:PatchTSTMasking
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patchtst/modeling_patchtst.py:PatchTSTEncoderLayer
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patchtst/modeling_patchtst.py:PatchTSTPreTrainedModel
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patchtst/modeling_patchtst.py:PatchTSTEmbedding
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patchtst/modeling_patchtst.py:PatchTSTPositionalEncoding
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patchtst/modeling_patchtst.py:PatchTSTEncoder
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patchtst/modeling_patchtst.py:PatchTSTModelOutput
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patchtst/modeling_patchtst.py:PatchTSTForPretrainingOutput
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patchtst/modeling_patchtst.py:PatchTSTForRegressionOutput
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patchtst/modeling_patchtst.py:PatchTSTForPredictionOutput
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[ "ModelForPredictionOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "loc", "loss", "prediction_outputs", "r", "scale" ]
patchtst/modeling_patchtst.py:PatchTSTForClassificationOutput
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[ "ModelForClassificationOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "loss", "prediction_logits", "r" ]
patchtst/modeling_patchtst.py:SamplePatchTSTOutput
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patchtst/modeling_patchtst.py:nll
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patchtst/modeling_patchtst.py:weighted_average
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patchtst/modeling_patchtst.py:PatchTSTStdScaler
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patchtst/modeling_patchtst.py:PatchTSTMeanScaler
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patchtst/modeling_patchtst.py:PatchTSTNOPScaler
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patchtst/modeling_patchtst.py:PatchTSTScaler
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patchtst/modeling_patchtst.py:PatchTSTModel
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[ "Identity", "ModelEncoder", "ModelMasking", "ModelModel", "ModelModelOutput", "ModelModelify", "ModelPreTrainedModel", "ModelScaler", "Model_input", "Modeled_values", "Modelifier", "None", "__init__", "attentions", "class", "config", "def", "do_mask_input", "else", "encoder", ...
patchtst/modeling_patchtst.py:PatchTSTMaskPretrainHead
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[ "Dropout", "Identity", "Linear", "ModelMaskPretrainHead", "Model_length", "Module", "__init__", "class", "config", "d_model", "def", "dropout", "else", "embedding", "forward", "head_dropout", "if", "linear", "nn", "return", "self", "super", "use_cls_token" ]
patchtst/modeling_patchtst.py:PatchTSTForPretraining
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[ "MSELoss", "ModelForPretraining", "ModelForPretrainingOutput", "ModelMaskPretrainHead", "ModelModel", "ModelPreTrainedModel", "Model_input", "None", "True", "__init__", "attentions", "class", "config", "def", "dim", "do_mask_input", "else", "encoder_states", "forward", "head", ...
patchtst/modeling_patchtst.py:PatchTSTClassificationHead
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[ "Dropout", "Flatten", "Identity", "Linear", "ModelClassificationHead", "Module", "__init__", "class", "config", "d_model", "def", "dim", "dropout", "elif", "else", "embedding", "flatten", "forward", "head_dropout", "if", "linear", "max", "mean", "nn", "num_input_chann...
patchtst/modeling_patchtst.py:PatchTSTForClassification
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[ "CrossEntropyLoss", "False", "ModelClassificationHead", "ModelForClassification", "ModelForClassificationOutput", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "attentions", "auto_docstring", "class", "config", "def", "do_mask_input", "else", "forward", "head"...
patchtst/modeling_patchtst.py:PatchTSTPredictionHead
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[ "Dropout", "Flatten", "Identity", "Linear", "ModelPredictionHead", "Module", "ModuleList", "None", "__init__", "append", "class", "config", "d_model", "def", "dim", "distribution_output", "dropout", "dropouts", "elif", "else", "embedding", "flatten", "flattens", "for", ...
patchtst/modeling_patchtst.py:PatchTSTForPrediction
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[ "False", "MSELoss", "ModelForPrediction", "ModelForPredictionOutput", "ModelModel", "ModelPreTrainedModel", "ModelPredictionHead", "Modelifier", "NegativeBinomialOutput", "None", "NormalOutput", "SampleModelOutput", "StudentTOutput", "True", "_", "__init__", "attentions", "class", ...
patchtst/modeling_patchtst.py:PatchTSTRegressionHead
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[ "Dropout", "Flatten", "Identity", "Linear", "ModelRegressionHead", "Module", "None", "__init__", "class", "config", "d_model", "def", "dim", "distribution_output", "dropout", "elif", "else", "embedding", "flatten", "forward", "get_parameter_projection", "head_dim", "head_...
patchtst/modeling_patchtst.py:PatchTSTForRegression
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[ "False", "MSELoss", "ModelForRegression", "ModelForRegressionOutput", "ModelModel", "ModelPreTrainedModel", "ModelRegressionHead", "NegativeBinomialOutput", "None", "NormalOutput", "SampleModelOutput", "StudentTOutput", "True", "_", "__init__", "attentions", "auto_docstring", "clas...
tapas/modeling_tapas.py:TableQuestionAnsweringOutput
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[ "ModelOutput", "ModelQuestionAnsweringOutput", "None", "attentions", "class", "hidden_states", "logits", "logits_aggregation", "loss", "r" ]
tapas/modeling_tapas.py:TapasEmbeddings
[ -0.00026421822258271277, 0.03016372211277485, 0.011254267767071724, -0.006141288205981255, -0.0012782448902726173, 0.03953275829553604, 0.021023200824856758, -0.005512877367436886, 0.006255544722080231, 0.005598569754511118, 0.028564130887389183, 0.026507513597607613, -0.000999744632281363, ...
[ "Dropout", "Embedding", "IndexMap", "LayerNorm", "ModelEmbeddings", "Module", "None", "ProductIndexMap", "__init__", "arange", "as_tensor", "batch_dims", "class", "col_index", "config", "def", "device", "dropout", "dtype", "else", "embeddings", "enumerate", "eps", "expa...
tapas/modeling_tapas.py:TapasSelfAttention
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[ "Dropout", "EncoderDecoderCache", "False", "Linear", "ModelSelfAttention", "Module", "None", "The", "True", "ValueError", "_", "__init__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob", "at...
tapas/modeling_tapas.py:TapasSelfOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelSelfOutput", "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" ]
tapas/modeling_tapas.py:TapasAttention
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[ "False", "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "__init__", "attention_mask", "attention_output", "cache_position", "class", "config", "def", "encoder_hidden_states", "forward", "hidden_states", "layer_idx", "nn", "output", "output_attentio...
tapas/modeling_tapas.py:TapasIntermediate
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[ "ACT2FN", "Linear", "ModelIntermediate", "Module", "__init__", "class", "config", "def", "dense", "else", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "intermediate_act_fn", "intermediate_size", "isinstance", "nn", "return", "self", "str", "super" ]
tapas/modeling_tapas.py:TapasOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "su...
tapas/modeling_tapas.py:TapasLayer
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[ "False", "GradientCheckpointingLayer", "If", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "True", "ValueError", "__init__", "add_cross_attention", "and", "apply_chunking_to_forward", "are", "attention", "attention_mask", "attention_output", "be", ...
tapas/modeling_tapas.py:TapasEncoder
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[ "BaseModelOutput", "DynamicCache", "EncoderDecoderCache", "False", "ModelEncoder", "ModelLayer", "Module", "ModuleList", "None", "True", "__init__", "all_attentions", "all_hidden_states", "and", "attention_mask", "attentions", "cache_position", "class", "config", "def", "else...
tapas/modeling_tapas.py:TapasPooler
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[ "Linear", "ModelPooler", "Module", "Tanh", "__init__", "activation", "class", "config", "def", "dense", "first_token_tensor", "forward", "hidden_size", "hidden_states", "nn", "pooled_output", "return", "self", "super" ]
tapas/modeling_tapas.py:TapasPredictionHeadTransform
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[ "ACT2FN", "LayerNorm", "Linear", "ModelPredictionHeadTransform", "Module", "__init__", "class", "config", "def", "dense", "else", "eps", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "isinstance", "layer_norm_eps", "nn", "return", "self", "str", "super"...
tapas/modeling_tapas.py:TapasLMPredictionHead
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[ "Linear", "ModelLMPredictionHead", "ModelPredictionHeadTransform", "Module", "Parameter", "__init__", "bias", "class", "config", "decoder", "def", "forward", "hidden_size", "hidden_states", "nn", "return", "self", "super", "torch", "transform", "vocab_size", "zeros" ]
tapas/modeling_tapas.py:TapasOnlyMLMHead
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[ "ModelLMPredictionHead", "ModelOnlyMLMHead", "Module", "__init__", "class", "config", "def", "forward", "nn", "prediction_scores", "predictions", "return", "self", "sequence_output", "super" ]
tapas/modeling_tapas.py:TapasPreTrainedModel
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[ "Model", "ModelConfig", "ModelLMPredictionHead", "ModelPreTrainedModel", "PreTrainedModel", "True", "_init_weights", "base_model_prefix", "bias", "class", "config", "def", "if", "init", "isinstance", "module", "no_grad", "self", "super", "supports_gradient_checkpointing", "to...
tapas/modeling_tapas.py:TapasModel
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[ "BaseModelOutputWithPooling", "ModelEmbeddings", "ModelEncoder", "ModelModel", "ModelPooler", "ModelPreTrainedModel", "None", "True", "_", "__init__", "add_pooling_layer", "and", "attention_mask", "attentions", "auto_docstring", "class", "config", "def", "device", "dtype", "e...
tapas/modeling_tapas.py:TapasForMaskedLM
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[ "CrossEntropyLoss", "False", "MaskedLMOutput", "Model", "ModelConfig", "ModelForMaskedLM", "ModelModel", "ModelOnlyMLMHead", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "base_model_prefix", ...
tapas/modeling_tapas.py:TapasForQuestionAnswering
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[ "Bernoulli", "Dropout", "EPSILON_ZERO_DIVISION", "False", "FloatTensor", "IndexMap", "Linear", "LongTensor", "Make", "Model", "ModelForQuestionAnswering", "ModelModel", "ModelPreTrainedModel", "None", "Parameter", "ProductIndexMap", "TableQuestionAnsweringOutput", "True", "ValueE...
tapas/modeling_tapas.py:TapasForSequenceClassification
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[ "BCEWithLogitsLoss", "CrossEntropyLoss", "Dropout", "Linear", "MSELoss", "Model", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "None", "SequenceClassifierOutput", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "class", "classifier", ...
tapas/modeling_tapas.py:AverageApproximationFunction
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[ "Enum", "FIRST_ORDER", "ModelApproximationFunction", "RATIO", "SECOND_ORDER", "class", "enum", "first_order", "ratio", "second_order", "str" ]
tapas/modeling_tapas.py:IndexMap
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[ "ModelMap", "as_tensor", "batch_dims", "batch_shape", "class", "def", "device", "indices", "num_segments", "return", "self", "size", "torch" ]
tapas/modeling_tapas.py:ProductIndexMap
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[ "IndexMap", "ModelIndexMap", "__init__", "batch_dims", "class", "def", "div", "float", "floor", "fmod", "if", "index", "indices", "inner_index", "long", "num_segments", "outer_index", "project_inner", "project_outer", "return", "rounding_mode", "self", "super", "torch",...
tapas/modeling_tapas.py:gather
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[ "Model", "batch_dims", "def", "else", "expand", "if", "index", "indices", "len", "name", "return", "segmented_Model", "shape", "size", "torch", "unsqueeze", "values", "view" ]
tapas/modeling_tapas.py:flatten
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[ "IndexMap", "Model", "_", "arange", "batch_dims", "batch_shape", "batch_size", "def", "device", "end", "for", "in", "index", "indices", "len", "list", "name", "num_segments", "offset", "prod", "range", "return", "segmented_Model", "size", "start", "tensor", "torch...
tapas/modeling_tapas.py:range_index_map
[ -0.00016673370555508882, -0.019432375207543373, -0.005812863353639841, 0.0015795824583619833, -0.0003053859400097281, 0.03819079324603081, -0.004240301437675953, -0.05077129229903221, 0.0026256171986460686, 0.006093678064644337, 0.00021675381867680699, -0.009210720658302307, -0.0036225090734...
[ "IndexMap", "Model_index_map", "aModel", "as_tensor", "assert", "batch_dims", "batch_shape", "cat", "cpu", "def", "device", "dim", "dtype", "else", "end", "for", "if", "in", "indices", "int", "is_tensor", "len", "list", "long", "multiples", "name", "new_shape", ...
tapas/modeling_tapas.py:_segment_reduce
[ -0.0002189266961067915, 0.0076271235011518, -0.02723165601491928, 0.0031073465943336487, -0.0006214693421497941, 0.008079101331532001, 0.012937861494719982, -0.01830509677529335, 0.009491532109677792, 0.015706224367022514, 0.0004413844726514071, 0.020677980035543442, -0.003983053378760815, ...
[ "False", "_segment_reduce", "as_tensor", "batch_shape", "cat", "clone", "def", "device", "dim", "dtype", "flat_index", "flat_values", "flatten", "flattened_shape", "float", "include_self", "index", "indices", "int", "len", "long", "name", "new_shape", "num_segments", ...
tapas/modeling_tapas.py:reduce_sum
[ 0.00002255815161333885, -0.008563734591007233, -0.011718794703483582, 0.04777662456035614, 0.00019278966647107154, 0.027156053110957146, 0.019155722111463547, -0.016113342717289925, 0.007662288844585419, -0.005746716633439064, 0.0055213551968336105, 0.001788806403055787, -0.00232403981499373...
[ "Model_sum", "_segment_Model", "def", "index", "name", "return", "segmented_Model_sum", "sum", "values" ]