class_docstr: Masked multi-pathway is composed of a list of stream nn.Modules followed by a
  fusion nn.Module that reduces these streams. Each stream module takes a mask
  and input tensor.
  
  ::
  
                          Pathway 1  ... Pathway N
                              ↓              ↓
                           Block 1        Block N
                              ↓⭠ --Fusion----↓
class_docstr_tok: ["Masked","multi-pathway","is","composed","of","a","list","of","stream","nn.Modules","followed","by","a","fusion","nn.Module","that","reduces","these","streams",".","Each","stream","module","takes","a","mask","and","input","tensor",".",":",":","Pathway","1","...","Pathway","N","\u2193","\u2193","Block","1","Block","N","\u2193\u2b60","--","Fusion","--","--","\u2193"]
docstr: Args:
      multipathway_blocks (nn.module_list): list of models from all pathways.
      multipathway_fusion (nn.module): fusion model.
docstr_tok: ["Args",":","multipathway_blocks","(","nn.module_list",")",":","list","of","models","from","all","pathways",".","multipathway_fusion","(","nn.module",")",":","fusion","model","."]
code: def __init__(
      self,
      *,
      multipathway_blocks: nn.ModuleList,
      multipathway_fusion: Optional[nn.Module],
  ) -> None:
      """
      Args:
          multipathway_blocks (nn.module_list): list of models from all pathways.
          multipathway_fusion (nn.module): fusion model.
      """
      super().__init__()
      set_attributes(self, locals())
  
imports: from typing import List, Optional, Tuple
  import torch
  from pytorchvideo.layers.utils import set_attributes
  from torch import nn
  from torch.nn.utils.rnn import pack_padded_sequence
label_desc: Use image node_id 1 to create a new MaskedMultiPathWay object from inherited base classes: nn with example: obj = MaskedMultiPathWay()