class_docstr: Concatenates all inputs by their temporal dimension which is assumed to be dim=1.
class_docstr_tok: ["Concatenates","all","inputs","by","their","temporal","dimension","which","is","assumed","to","be","dim=1","."]
docstr: Args:
      input_list (List[torch.Tensor]): a list of tensors of shape
          (batch_size, seq_len, feature_dim)
  
  Returns:
      Tensor of shape (batch_size, sum(seq_len), feature_dim) where sum(seq_len) is
          the sum of all input tensors.
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code: def forward(self, input_list: List[torch.Tensor]) -> torch.Tensor:
      """
      Args:
          input_list (List[torch.Tensor]): a list of tensors of shape
              (batch_size, seq_len, feature_dim)
  
      Returns:
          Tensor of shape (batch_size, sum(seq_len), feature_dim) where sum(seq_len) is
              the sum of all input tensors.
      """
      return torch.cat(input_list, dim=1)
  
imports: from typing import Callable, List
  import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the TemporalConcatFusion obj's underlying member method code with example usage: obj.forward(input_list) and returns: torch