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 __init__(self, input_channels: int, num_classes: int):
      super().__init__()
  
      self.dsconv1 = layers.SeparableConvBNReLU(
          in_channels=input_channels,
          out_channels=input_channels,
          kernel_size=3,
          padding=1,
      )
  
      self.dsconv2 = layers.SeparableConvBNReLU(
          in_channels=input_channels,
          out_channels=input_channels,
          kernel_size=3,
          padding=1,
      )
  
      self.conv = nn.Conv2D(
          in_channels=input_channels,
          out_channels=num_classes,
          kernel_size=1,
      )
  
      self.dropout = nn.Dropout(p=0.1)
  
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 1 to create a new Classifier object from inherited base classes: nn with example: obj = Classifier(input_channels, num_classes)