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]
      fuse = self.conv_fast_to_slow(x_f)
      if self.norm is not None:
          fuse = self.norm(fuse)
      if self.activation is not None:
          fuse = self.activation(fuse)
      x_s_fuse = torch.cat([x_s, fuse], 1)
      return [x_s_fuse, x_f]
  
imports: from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from pytorchvideo.layers.utils import set_attributes
  from pytorchvideo.models.head import create_res_basic_head, create_res_roi_pooling_head
  from pytorchvideo.models.net import DetectionBBoxNetwork, MultiPathWayWithFuse, Net
  from pytorchvideo.models.resnet import create_bottleneck_block, create_res_stage
  from pytorchvideo.models.stem import create_res_basic_stem
label_desc: Use image node_id 2 for calling the FuseFastToSlow obj's underlying member method code with example usage: obj.forward(x) and returns: list