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","."]
code: def __init__(self, feature_dims: List[int]):
      super().__init__()
      _verify_feature_dim(feature_dims)
  
      # All input dimensions must be the same
      self._output_dim = max(feature_dims)
      assert self._output_dim == min(feature_dims)
  
imports: from typing import Callable, List
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new TemporalConcatFusion object from inherited base classes: nn with example: obj = TemporalConcatFusion(feature_dims)