code: def forward(self, x):
      H, W = x.size()[2:]
  
      x = self.init_block(x)
  
      x1 = self.block1_1(x)
      x1 = self.block1_2(x1)
      x1 = self.block1_3(x1)
  
      x2 = self.block2_1(x1)
      x2 = self.block2_2(x2)
      x2 = self.block2_3(x2)
      x2 = self.block2_4(x2)
  
      x3 = self.block3_1(x2)
      x3 = self.block3_2(x3)
      x3 = self.block3_3(x3)
      x3 = self.block3_4(x3)
  
      x4 = self.block4_1(x3)
      x4 = self.block4_2(x4)
      x4 = self.block4_3(x4)
      x4 = self.block4_4(x4)
  
      x_fuses = []
      if self.sa and self.dil is not None:
          for i, xi in enumerate([x1, x2, x3, x4]):
              x_fuses.append(self.attentions[i](self.dilations[i](xi)))
      elif self.sa:
          for i, xi in enumerate([x1, x2, x3, x4]):
              x_fuses.append(self.attentions[i](xi))
      elif self.dil is not None:
          for i, xi in enumerate([x1, x2, x3, x4]):
              x_fuses.append(self.dilations[i](xi))
      else:
          x_fuses = [x1, x2, x3, x4]
  
      e1 = self.conv_reduces[0](x_fuses[0])
      e1 = F.interpolate(
          e1, (H, W), mode="bilinear", align_corners=False
      )
  
      e2 = self.conv_reduces[1](x_fuses[1])
      e2 = F.interpolate(
          e2, (H, W), mode="bilinear", align_corners=False
      )
  
      e3 = self.conv_reduces[2](x_fuses[2])
      e3 = F.interpolate(
          e3, (H, W), mode="bilinear", align_corners=False
      )
  
      e4 = self.conv_reduces[3](x_fuses[3])
      e4 = F.interpolate(
          e4, (H, W), mode="bilinear", align_corners=False
      )
  
      outputs = [e1, e2, e3, e4]
  
      output = self.classifier(torch.cat(outputs, dim=1))
      # if not self.training:
      #    return torch.sigmoid(output)
  
      outputs.append(output)
      outputs = [torch.sigmoid(r) for r in outputs]
      return outputs
  
imports: import os
  import math
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from typing import Optional
  from cftool.cv import to_uint8
  from ....parameters import OPT
label_desc: Use image node_id 3 for calling the PiDiNet obj's underlying member method code with example usage: obj.forward(x) and returns: outputs