class_docstr: Residual block. Performs a summation between an identity shortcut in branch1 and a
  main block in branch2. When the input and output dimensions are different, a
  convolution followed by a normalization will be performed.
  
  ::
  
  
                                       Input
                                         |-------+
                                         ↓       |
                                       Block     |
                                         ↓       |
                                     Summation ←-+
                                         ↓
                                     Activation
  
  The builder can be found in `create_res_block`.
docstr: Args:
      branch1_conv (torch.nn.modules): convolutional module in branch1.
      branch1_norm (torch.nn.modules): normalization module in branch1.
      branch2 (torch.nn.modules): bottleneck block module in branch2.
      activation (torch.nn.modules): activation module.
      branch_fusion: (Callable): A callable or layer that combines branch1
          and branch2.
docstr_tok: ["Args",":","branch1_conv","(","torch.nn.modules",")",":","convolutional","module","in","branch1",".","branch1_norm","(","torch.nn.modules",")",":","normalization","module","in","branch1",".","branch2","(","torch.nn.modules",")",":","bottleneck","block","module","in","branch2",".","activation","(","torch.nn.modules",")",":","activation","module",".","branch_fusion",":","(","Callable",")",":","A","callable","or","layer","that","combines","branch1","and","branch2","."]
code: def __init__(
      self,
      branch1_conv: nn.Module = None,
      branch1_norm: nn.Module = None,
      branch2: nn.Module = None,
      activation: nn.Module = None,
      branch_fusion: Callable = None,
  ) -> nn.Module:
      """
      Args:
          branch1_conv (torch.nn.modules): convolutional module in branch1.
          branch1_norm (torch.nn.modules): normalization module in branch1.
          branch2 (torch.nn.modules): bottleneck block module in branch2.
          activation (torch.nn.modules): activation module.
          branch_fusion: (Callable): A callable or layer that combines branch1
              and branch2.
      """
      super().__init__()
      set_attributes(self, locals())
      assert self.branch2 is not None
  
imports: from typing import Callable, List, Tuple, Union
  import numpy
  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, Net
  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 ResBlock object from inherited base classes: nn with example: obj = ResBlock(branch1_conv, branch1_norm, branch2, activation, branch_fusion)