class_docstr: Residual Dense Block
  The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
  Modified options that can be used:
      - "Partial Convolution based Padding" arXiv:1811.11718
      - "Spectral normalization" arXiv:1802.05957
      - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
          {Rakotonirina} and A. {Rasoanaivo}
class_docstr_tok: ["Residual","Dense","Block","The","core","module","of","paper",":","(","Residual","Dense","Network","for","Image","Super-Resolution",",","CVPR","18",")","Modified","options","that","can","be","used",":","-","``","Partial","Convolution","based","Padding","''","arXiv:1811.11718","-","``","Spectral","normalization","''","arXiv:1802.05957","-","``","ICASSP","2020","-","ESRGAN+",":","Further","Improving","ESRGAN","''","N.","C.","{","Rakotonirina","}","and","A",".","{","Rasoanaivo","}"]
code: def forward(self, x):
      x1 = self.conv1(x)
      x2 = self.conv2(torch.cat((x, x1), 1))
      if self.conv1x1:
          x2 = x2 + self.conv1x1(x)
      x3 = self.conv3(torch.cat((x, x1, x2), 1))
      x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
      if self.conv1x1:
          x4 = x4 + x2
      x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
      if self.noise:
          return self.noise(x5.mul(0.2) + x)
      else:
          return x5 * 0.2 + x
  
imports: from collections import OrderedDict
  import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ResidualDenseBlock_5C obj's underlying member method code with example usage: obj.forward(x) and returns: self, unknown