code: def _make_level(
      self,
      block,
      inplanes,
      planes,
      blocks,
      norm_layer,
      norm_kwargs,
      stride=1,
  ):
      downsample = None
      if stride != 1 or inplanes != planes:
          downsample = nn.HybridSequential()
          downsample.add(
              *[
                  nn.MaxPool2D(stride, strides=stride),
                  nn.Conv2D(
                      channels=planes,
                      in_channels=inplanes,
                      kernel_size=1,
                      strides=1,
                      use_bias=False,
                  ),
                  norm_layer(in_channels=planes, **norm_kwargs),
              ]
          )
  
      layers = []
      layers.append(
          block(
              inplanes,
              planes,
              stride,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
              downsample=downsample,
          )
      )
      for _ in range(1, blocks):
          layers.append(
              block(
                  inplanes,
                  planes,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
  
      curr_level = nn.HybridSequential()
      curr_level.add(*layers)
      return curr_level
  
imports: from __future__ import division
  import os
  import mxnet
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 2 for calling the DLA obj's underlying member method code with example usage: obj._make_level(block, inplanes, planes, blocks, norm_layer, norm_kwargs, stride) and returns: curr_level