class_docstr: FastSCNN classifier
class_docstr_tok: ["FastSCNN","classifier"]
code: def __init__(
      self,
      dw_channels,
      num_classes,
      stride=1,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(Classifer, self).__init__()
      with self.name_scope():
          self.dsconv1 = _DSConv(
              dw_channels,
              dw_channels,
              stride,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
          )
          self.dsconv2 = _DSConv(
              dw_channels,
              dw_channels,
              stride,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
          )
          self.dp = nn.Dropout(0.1)
          self.conv = nn.Conv2D(
              in_channels=dw_channels,
              channels=num_classes,
              kernel_size=1,
          )
  
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 Classifer object from inherited base classes: HybridBlock with example: obj = Classifer(dw_channels, num_classes, stride, norm_layer, norm_kwargs)