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,
      conv_fast_to_slow: nn.Module,
      norm: Optional[nn.Module] = None,
      activation: Optional[nn.Module] = None,
  ) -> 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())
  
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 1 to create a new FuseFastToSlow object from inherited base classes: nn with example: obj = FuseFastToSlow(conv_fast_to_slow, norm, activation)