class_docstr: This class defines the ESP block, which is based on the following principle
      Reduce ---> Split ---> Transform --> Merge
docstr: :param input_: input feature map
  :return: transformed feature map
docstr_tok: [":","param","input_",":","input","feature","map",":","return",":","transformed","feature","map"]
code: def forward(self, input_):
      """
      :param input_: input feature map
      :return: transformed feature map
      """
      # reduce
      output1 = self.c1(input_)
      # split and transform
      d1 = self.d1(output1)
      d2 = self.d2(output1)
      d4 = self.d4(output1)
      d8 = self.d8(output1)
      d16 = self.d16(output1)
  
      # heirarchical fusion for de-gridding
      add1 = d2
      add2 = add1 + d4
      add3 = add2 + d8
      add4 = add3 + d16
  
      # merge
      combine = torch.cat([d1, add1, add2, add3, add4], 1)
  
      # if residual version
      if self.add:
          combine = input_ + combine
      output = self.bn(combine)
      return output
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the DilatedParllelResidualBlockB obj's underlying member method code with example usage: obj.forward(input_) and returns: output