class_docstr: This class defines the ESPNet network
docstr: :param input_: RGB image
  :return: transformed feature map
docstr_tok: [":","param","input_",":","RGB","image",":","return",":","transformed","feature","map"]
code: def forward(self, input_):
      """
      :param input_: RGB image
      :return: transformed feature map
      """
      output0 = self.modules[0](input_)
      inp1 = self.modules[1](input_)
      inp2 = self.modules[2](input_)
  
      output0_cat = self.modules[3](torch.cat([output0, inp1], 1))
      output1_0 = self.modules[4](output0_cat)  # down-sampled
  
      for i, layer in enumerate(self.modules[5]):
          if i == 0:
              output1 = layer(output1_0)
          else:
              output1 = layer(output1)
  
      output1_cat = self.modules[6](
          torch.cat([output1, output1_0, inp2], 1)
      )
  
      output2_0 = self.modules[7](output1_cat)  # down-sampled
      for i, layer in enumerate(self.modules[8]):
          if i == 0:
              output2 = layer(output2_0)
          else:
              output2 = layer(output2)
  
      output2_cat = self.modules[9](
          torch.cat([output2_0, output2], 1)
      )  # concatenate for feature map width expansion
  
      output2_c = self.up_l3(
          self.br(self.modules[10](output2_cat))
      )  # RUM
  
      output1_C = self.level3_C(
          output1_cat
      )  # project to C-dimensional space
      comb_l2_l3 = self.up_l2(
          self.combine_l2_l3(torch.cat([output1_C, output2_c], 1))
      )  # RUM
  
      concat_features = self.conv(
          torch.cat([comb_l2_l3, output0_cat], 1)
      )
  
      classifier = self.classifier(concat_features)
      return classifier
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ESPNet obj's underlying member method code with example usage: obj.forward(input_) and returns: classifier