code: def _make_layer__nostride_dilate(
      self,
      block,
      norm_layer,
      planes,
      blocks,
      stride=1,
      bn_eps=1e-5,
      bn_momentum=0.1,
  ):
      downsample = nn.Sequential(
          nn.Conv2D(
              self.inplanes,
              planes * block.expansion,
              kernel_size=1,
              stride=1,
              bias_attr=False,
          ),
          norm_layer(
              planes * block.expansion,
              epsilon=bn_eps,
              momentum=bn_momentum,
          ),
      )
  
      dilate = 2
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              norm_layer,
              bn_eps,
              bn_momentum,
              downsample,
              dilate,
          )
      )
  
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
          dilate *= 2
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  norm_layer=norm_layer,
                  bn_eps=bn_eps,
                  bn_momentum=bn_momentum,
                  dilate=dilate,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import paddle.nn
  from paddleseg.utils import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 3 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer__nostride_dilate(block, norm_layer, planes, blocks, stride, bn_eps, bn_momentum) and returns: nn