class_docstr: Given a list of two tensors from Slow pathway and Fast pathway, fusion information
  from the Fast pathway to the Slow on through a convolution followed by a
  concatenation, then return the fused list of tensors from Slow and Fast pathway in
  order.
class_docstr_tok: ["Given","a","list","of","two","tensors","from","Slow","pathway","and","Fast","pathway",",","fusion","information","from","the","Fast","pathway","to","the","Slow","on","through","a","convolution","followed","by","a","concatenation",",","then","return","the","fused","list","of","tensors","from","Slow","and","Fast","pathway","in","order","."]
docstr: Args:
      conv_fast_to_slow (nn.module): convolution to perform fusion.
      norm (nn.module): normalization module.
      activation (torch.nn.modules): activation module.
docstr_tok: ["Args",":","conv_fast_to_slow","(","nn.module",")",":","convolution","to","perform","fusion",".","norm","(","nn.module",")",":","normalization","module",".","activation","(","torch.nn.modules",")",":","activation","module","."]
code: def __init__(
      self,
      block_fast_to_slow: nn.Module,
      block_audio_to_fastslow: nn.Module,
  ) -> None:
      """
      Args:
          conv_fast_to_slow (nn.module): convolution to perform fusion.
          norm (nn.module): normalization module.
          activation (torch.nn.modules): activation module.
      """
      super().__init__()
      set_attributes(self, locals())
  
file_path: pytorchvideo/pytorchvideo/models/audio_visual_slowfast.py
imports: from typing import Callable, Tuple, Union
  import torch
  import torch.nn
  from pytorchvideo.layers.utils import set_attributes
  from pytorchvideo.models.resnet import create_acoustic_bottleneck_block, create_bottleneck_block
  from pytorchvideo.models.slowfast import create_slowfast
  from pytorchvideo.models.stem import create_acoustic_res_basic_stem, create_res_basic_stem
label_desc: Use image node_id 1 to create a new FuseAudioToFastSlow object from inherited base classes: nn with example: obj = FuseAudioToFastSlow(block_fast_to_slow, block_audio_to_fastslow)