class_docstr: The ESPNet-C implementation based on PaddlePaddle.
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): Number of input channels. Default: 3.
      level2_depth (int, optional): Depth of DilatedResidualBlock. Default: 5.
      level3_depth (int, optional): Depth of DilatedResidualBlock. Default: 3.
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code: def __init__(
      self, num_classes, in_channels=3, level2_depth=5, level3_depth=3
  ):
      super().__init__()
      self.level1 = layers.ConvBNPReLU(
          in_channels, 16, 3, padding="same", stride=2
      )
      self.br1 = BNPReLU(in_channels + 16)
      self.proj1 = layers.ConvBNPReLU(in_channels + 16, num_classes, 1)
  
      self.level2_0 = DownSampler(in_channels + 16, 64)
      self.level2 = nn.Sequential(
          *[DilatedResidualBlock(64, 64) for i in range(level2_depth)]
      )
      self.br2 = BNPReLU(in_channels + 128)
      self.proj2 = layers.ConvBNPReLU(in_channels + 128, num_classes, 1)
  
      self.level3_0 = DownSampler(in_channels + 128, 128)
      self.level3 = nn.Sequential(
          *[DilatedResidualBlock(128, 128) for i in range(level3_depth)]
      )
      self.br3 = BNPReLU(256)
      self.proj3 = layers.ConvBNPReLU(256, num_classes, 1)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new ESPNetEncoder object from inherited base classes: nn with example: obj = ESPNetEncoder(num_classes, in_channels, level2_depth, level3_depth)