code: def _make_layer(self, num_blocks, stride):
      strides = [stride] + [1] * (num_blocks - 1)
      layers = []
      for stride in strides:
          layers.append(
              Block(
                  self.in_planes,
                  self.cardinality,
                  self.bottleneck_width,
                  stride,
              )
          )
          self.in_planes = (
              Block.expansion * self.cardinality * self.bottleneck_width
          )
      # Increase bottleneck_width by 2 after each stage.
      self.bottleneck_width *= 2
      return nn.Sequential(*layers)
  
file_path: nni/examples/trials/cifar10_pytorch/models/resnext.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ResNeXt obj's underlying member method code with example usage: obj._make_layer(num_blocks, stride) and returns: nn