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","."]
code: def forward(self, x):
      x_s = x[0]
      x_f = x[1]
      x_a = x[2]
      fuse = self.block_fast_to_slow(x_f)
  
      # Reduce frequency dim
      average_a = torch.mean(x_a, dim=-1, keepdim=True)
      fuse_a = self.block_audio_to_fastslow(average_a)
  
      x_s_fuse = torch.cat([x_s, fuse], 1)
      print(x_s_fuse.size())
      return [fuse_a + x_s_fuse, x_f, x_a]
  
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 2 for calling the FuseAudioToFastSlow obj's underlying member method code with example usage: obj.forward(x) and returns: list