code: def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
      downsample = None
      if (
          stride != 1
          or self.inplanes != planes * block.expansion
          or dilation == 2
          or dilation == 4
      ):
          downsample = nn.Sequential(
              nn.Conv2D(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias_attr=False,
                  weight_attr=self.weight_attr_conv,
              ),
              nn.BatchNorm2D(
                  planes * block.expansion,
                  bias_attr=self.bias_attr,
                  weight_attr=self.weight_attr,
              ),
          )
      nnlayers = nn.LayerList()
      nnlayers.append(
          block(
              self.inplanes,
              planes,
              stride,
              dilation=dilation,
              downsample=downsample,
          )
      )
      self.inplanes = planes * block.expansion
  
      for i in range(1, blocks):
          nnlayers.append(
              block(self.inplanes, planes, dilation=dilation)
          )
  
      return nn.Sequential(*nnlayers)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the ResNetMulti obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, dilation) and returns: nn