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.
code: def __init__(self, input_channels, num_classes):
      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 paddle.nn
  import paddle.nn.functional
  import paddle
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
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)