code: def __init__(
      self,
      in_channels,
      atrous_rates,
      norm_layer,
      norm_kwargs,
      height=64,
      width=64,
  ):
      super(_ASPP, self).__init__()
      out_channels = 256
      b0 = nn.HybridSequential()
      with b0.name_scope():
          b0.add(
              nn.Conv2D(
                  in_channels=in_channels,
                  channels=out_channels,
                  kernel_size=1,
                  use_bias=False,
              )
          )
          b0.add(
              norm_layer(
                  in_channels=out_channels,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          b0.add(nn.Activation("relu"))
  
      rate1, rate2, rate3 = tuple(atrous_rates)
      b1 = _ASPPConv(
          in_channels, out_channels, rate1, norm_layer, norm_kwargs
      )
      b2 = _ASPPConv(
          in_channels, out_channels, rate2, norm_layer, norm_kwargs
      )
      b3 = _ASPPConv(
          in_channels, out_channels, rate3, norm_layer, norm_kwargs
      )
      b4 = _AsppPooling(
          in_channels,
          out_channels,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          height=height,
          width=width,
      )
  
      self.concurent = gluon.contrib.nn.HybridConcurrent(axis=1)
      with self.concurent.name_scope():
          self.concurent.add(b0)
          self.concurent.add(b1)
          self.concurent.add(b2)
          self.concurent.add(b3)
          self.concurent.add(b4)
  
      self.project = nn.HybridSequential()
      with self.project.name_scope():
          self.project.add(
              nn.Conv2D(
                  in_channels=5 * out_channels,
                  channels=out_channels,
                  kernel_size=1,
                  use_bias=False,
              )
          )
          self.project.add(
              norm_layer(
                  in_channels=out_channels,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          self.project.add(nn.Activation("relu"))
          self.project.add(nn.Dropout(0.5))
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from mxnet import gluon
  from .fcn import _FCNHead
  from .xception import get_xcetption
label_desc: Use image node_id 1 to create a new _ASPP object from inherited base classes: nn with example: obj = _ASPP(in_channels, atrous_rates, norm_layer, norm_kwargs, height, width)