class_docstr: This class defines the ESPNet-C network in the paper
docstr: :param classes: number of classes in the dataset. Default is 20 for the cityscapes
  :param p: depth multiplier
  :param q: depth multiplier
docstr_tok: [":","param","classes",":","number","of","classes","in","the","dataset",".","Default","is","20","for","the","cityscapes",":","param","p",":","depth","multiplier",":","param","q",":","depth","multiplier"]
code: def __init__(self, classes=20, p=5, q=3):
      """
      :param classes: number of classes in the dataset. Default is 20 for the cityscapes
      :param p: depth multiplier
      :param q: depth multiplier
      """
      super().__init__()
      self.level1 = CBR(3, 16, 3, 2)
      self.sample1 = InputProjectionA(1)
      self.sample2 = InputProjectionA(2)
  
      self.b1 = BR(16 + 3)
      self.level2_0 = DownSamplerB(16 + 3, 64)
  
      self.level2 = nn.ModuleList()
      for i in range(0, p):
          self.level2.append(DilatedParllelResidualBlockB(64, 64))
      self.b2 = BR(128 + 3)
  
      self.level3_0 = DownSamplerB(128 + 3, 128)
      self.level3 = nn.ModuleList()
      for i in range(0, q):
          self.level3.append(DilatedParllelResidualBlockB(128, 128))
      self.b3 = BR(256)
  
      self.classifier = C(256, classes, 1, 1)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new ESPNet_Encoder object from inherited base classes: nn with example: obj = ESPNet_Encoder(classes, p, q)