class_docstr: This class defines the ESPNet-C network in the paper
docstr: :param input_: Receives the input RGB image
  :return: the transformed feature map with spatial dimensions 1/8th of the input image
docstr_tok: [":","param","input_",":","Receives","the","input","RGB","image",":","return",":","the","transformed","feature","map","with","spatial","dimensions","1\/8th","of","the","input","image"]
code: def forward(self, input_):
      """
      :param input_: Receives the input RGB image
      :return: the transformed feature map with spatial dimensions 1/8th of the input image
      """
      output0 = self.level1(input_)
      inp1 = self.sample1(input_)
      inp2 = self.sample2(input_)
  
      output0_cat = self.b1(torch.cat([output0, inp1], 1))
      output1_0 = self.level2_0(output0_cat)  # down-sampled
  
      for i, layer in enumerate(self.level2):
          if i == 0:
              output1 = layer(output1_0)
          else:
              output1 = layer(output1)
  
      output1_cat = self.b2(torch.cat([output1, output1_0, inp2], 1))
  
      output2_0 = self.level3_0(output1_cat)  # down-sampled
      for i, layer in enumerate(self.level3):
          if i == 0:
              output2 = layer(output2_0)
          else:
              output2 = layer(output2)
  
      output2_cat = self.b3(torch.cat([output2_0, output2], 1))
  
      classifier = self.classifier(output2_cat)
  
      return classifier
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ESPNet_Encoder obj's underlying member method code with example usage: obj.forward(input_) and returns: classifier