code: def _make_layer(self, block, planes, blocks, stride=1):
      if blocks == 0:
          return nn.Sequential(nn.Identity())
      norm_layer = self._norm_layer
      upsample = None
      if stride != 1:
          upsample = nn.Sequential(
              nn.UpsamplingNearest2D(scale_factor=2),
              nn.utils.spectral_norm(
                  conv1x1(self.inplanes, planes * block.expansion)
              ),
              norm_layer(planes * block.expansion),
          )
      elif self.inplanes != planes * block.expansion:
          upsample = nn.Sequential(
              nn.utils.spectral_norm(
                  conv1x1(self.inplanes, planes * block.expansion)
              ),
              norm_layer(planes * block.expansion),
          )
  
      layers = [
          block(
              self.inplanes,
              planes,
              stride,
              upsample,
              norm_layer,
              self.large_kernel,
          )
      ]
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  norm_layer=norm_layer,
                  large_kernel=self.large_kernel,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
  from ppmatting.models.layers import GuidedCxtAtten
label_desc: Use image node_id 2 for calling the ResNet_D_Dec obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride) and returns: nn, nn