class_docstr: The Classifier module implementation.
  This module consists of two depth-wise conv and one conv.
  Args:
      input_channels (int): The input channels to this module.
      num_classes (int): The unique number of target classes.
class_docstr_tok: ["The","Classifier","module","implementation",".","This","module","consists","of","two","depth-wise","conv","and","one","conv",".","Args",":","input_channels","(","int",")",":","The","input","channels","to","this","module",".","num_classes","(","int",")",":","The","unique","number","of","target","classes","."]
code: def forward(self, x: paddle.Tensor) -> paddle.Tensor:
      x = self.dsconv1(x)
      x = self.dsconv2(x)
      x = self.dropout(x)
      x = self.conv(x)
      return x
  
imports: import os
  from typing import Callable, Union, Tuple
  import paddle.nn
  import paddle.nn.functional
  import paddle
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  import fastscnn_cityscapes.layers
label_desc: Use image node_id 2 for calling the Classifier obj's underlying member method code with example usage: obj.forward(x) and returns: x