code: def _make_layer(
      self,
      block,
      planes,
      blocks,
      kernel_size=56,
      stride=1,
      dilate=False,
  ):
      norm_layer = self._norm_layer
      downsample = None
      previous_dilation = self.dilation
      if dilate:
          self.dilation *= stride
          stride = 1
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              conv1x1(self.inplanes, planes * block.expansion, stride),
              norm_layer(planes * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              downsample,
              groups=self.groups,
              base_width=self.base_width,
              dilation=previous_dilation,
              norm_layer=norm_layer,
              kernel_size=kernel_size,
          )
      )
      self.inplanes = planes * block.expansion
      if stride != 1:
          kernel_size = kernel_size // 2
  
      for _ in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  groups=self.groups,
                  base_width=self.base_width,
                  dilation=self.dilation,
                  norm_layer=norm_layer,
                  kernel_size=kernel_size,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the AxialAttentionNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, kernel_size, stride, dilate) and returns: nn