code: def _ASPPConv(
      in_channels, out_channels, atrous_rate, norm_layer, norm_kwargs
  ):
      block = nn.HybridSequential()
      with block.name_scope():
          block.add(
              nn.Conv2D(
                  in_channels=in_channels,
                  channels=out_channels,
                  kernel_size=3,
                  padding=atrous_rate,
                  dilation=atrous_rate,
                  use_bias=False,
              )
          )
          block.add(
              norm_layer(
                  in_channels=out_channels,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          block.add(nn.Activation("relu"))
      return block
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from .wideresnet import wider_resnet38_a2
label_desc: Use image node_id 1 for calling a global function with example usage: _ASPPConv(in_channels, out_channels, atrous_rate, norm_layer, norm_kwargs) and returns: block