class_docstr: Build multi-pathway block with fusion for video recognition, each of the pathway
  contains its own Blocks and Fusion layers across different pathways.
  
  ::
  
                          Pathway 1  ... Pathway N
                              ↓              ↓
                           Block 1        Block N
                              ↓⭠ --Fusion----↓
class_docstr_tok: ["Build","multi-pathway","block","with","fusion","for","video","recognition",",","each","of","the","pathway","contains","its","own","Blocks","and","Fusion","layers","across","different","pathways",".",":",":","Pathway","1","...","Pathway","N","\u2193","\u2193","Block","1","Block","N","\u2193\u2b60","--","Fusion","--","--","\u2193"]
code: def forward(self, x: List[torch.Tensor]) -> torch.Tensor:
      assert isinstance(
          x, list
      ), "input for MultiPathWayWithFuse needs to be a list of tensors"
      if self.inplace:
          x_out = x
      else:
          x_out = [None] * len(x)
      for pathway_idx in range(len(self.multipathway_blocks)):
          if self.multipathway_blocks[pathway_idx] is not None:
              x_out[pathway_idx] = self.multipathway_blocks[
                  pathway_idx
              ](x[pathway_idx])
      if self.multipathway_fusion is not None:
          x_out = self.multipathway_fusion(x_out)
      return x_out
  
file_path: pytorchvideo/pytorchvideo/models/net.py
imports: from typing import List, Optional
  import torch
  import torch.nn
  from pytorchvideo.layers.utils import set_attributes
  from pytorchvideo.models.weight_init import init_net_weights
label_desc: Use image node_id 2 for calling the MultiPathWayWithFuse obj's underlying member method code with example usage: obj.forward(x) and returns: x_out