code: def _make_transition_layer(
      self, num_channels_pre_layer, num_channels_cur_layer
  ):
      num_branches_cur = len(num_channels_cur_layer)
      num_branches_pre = len(num_channels_pre_layer)
  
      transition_layers = []
      for i in range(num_branches_cur):
          if i < num_branches_pre:
              if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                  transition_layers.append(
                      nn.Sequential(
                          nn.Conv2d(
                              num_channels_pre_layer[i],
                              num_channels_cur_layer[i],
                              3,
                              1,
                              1,
                              bias=False,
                          ),
                          self.norm_layer(num_channels_cur_layer[i]),
                          nn.ReLU(inplace=True),
                      )
                  )
              else:
                  transition_layers.append(None)
          else:
              conv3x3s = []
              for j in range(i + 1 - num_branches_pre):
                  inchannels = num_channels_pre_layer[-1]
                  outchannels = (
                      num_channels_cur_layer[i]
                      if j == i - num_branches_pre
                      else inchannels
                  )
                  conv3x3s.append(
                      nn.Sequential(
                          nn.Conv2d(
                              inchannels,
                              outchannels,
                              3,
                              2,
                              1,
                              bias=False,
                          ),
                          self.norm_layer(outchannels),
                          nn.ReLU(inplace=True),
                      )
                  )
              transition_layers.append(nn.Sequential(*conv3x3s))
  
      return nn.ModuleList(transition_layers)
  
imports: import numpy
  import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from torchvision.models.utils import load_state_dict_from_url
label_desc: Use image node_id 2 for calling the HighResolutionNet obj's underlying member method code with example usage: obj._make_transition_layer(num_channels_pre_layer, num_channels_cur_layer) and returns: nn