code: def _make_conv_level(
      self,
      inplanes,
      planes,
      convs,
      norm_layer,
      norm_kwargs,
      stride=1,
      dilation=1,
  ):
      modules = []
      for i in range(convs):
          modules.extend(
              [
                  nn.Conv2D(
                      in_channels=inplanes,
                      channels=planes,
                      kernel_size=3,
                      strides=stride if i == 0 else 1,
                      padding=dilation,
                      use_bias=False,
                      dilation=dilation,
                  ),
                  norm_layer(**norm_kwargs),
                  nn.Activation("relu"),
              ]
          )
          inplanes = planes
      curr_level = nn.HybridSequential()
      curr_level.add(*modules)
      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 3 for calling the DLA obj's underlying member method code with example usage: obj._make_conv_level(inplanes, planes, convs, norm_layer, norm_kwargs, stride, dilation) and returns: curr_level