class_docstr: Generic fusion method which takes a callable which takes the list of input tensors
  and expects a single tensor to be used. This class can be used to implement fusion
  methods like "sum", "max" and "prod".
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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, seq_len, feature_dim).
docstr_tok: ["Args",":","input_list","(","List","[","torch.Tensor","]",")",":","a","list","of","tensors","of","shape","(","batch_size",",","seq_len",",","feature_dim",")",".","Returns",":","Tensor","of","shape","(","batch_size",",","seq_len",",","feature_dim",")","."]
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, seq_len, feature_dim).
      """
      return self.reduce_fn(torch.stack(input_list))
  
imports: from typing import Callable, List
  import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the ReduceFusion obj's underlying member method code with example usage: obj.forward(input_list) and returns: self