code: def __init__(
      self,
      dw_channels,
      out_channels,
      stride=1,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
  ):
      super(_DWConv, self).__init__()
      with self.name_scope():
          self.conv = nn.HybridSequential()
          self.conv.add(
              nn.Conv2D(
                  in_channels=dw_channels,
                  channels=out_channels,
                  kernel_size=3,
                  strides=stride,
                  padding=1,
                  groups=dw_channels,
                  use_bias=False,
              )
          )
          self.conv.add(
              norm_layer(
                  in_channels=out_channels,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          self.conv.add(nn.Activation("relu"))
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new _DWConv object from inherited base classes: HybridBlock with example: obj = _DWConv(dw_channels, out_channels, stride, norm_layer, norm_kwargs)