code: def forward(self, x):
      out = self.conv0p1s1(x)
      out = self.bn0(out)
      out_p1 = self.relu(out)
  
      out = self.conv1p1s2(out_p1)
      out = self.bn1(out)
      out = self.relu(out)
      out_b1p2 = self.block1(out)
  
      out = self.conv2p2s2(out_b1p2)
      out = self.bn2(out)
      out = self.relu(out)
      out_b2p4 = self.block2(out)
  
      out = self.conv3p4s2(out_b2p4)
      out = self.bn3(out)
      out = self.relu(out)
      out_b3p8 = self.block3(out)
  
      # tensor_stride=16
      out = self.conv4p8s2(out_b3p8)
      out = self.bn4(out)
      out = self.relu(out)
      out = self.block4(out)
  
      # tensor_stride=8
      out = self.convtr4p16s2(out)
      out = self.bntr4(out)
      out = self.relu(out)
  
      out = ME.cat(out, out_b3p8)
      out = self.block5(out)
  
      # tensor_stride=4
      out = self.convtr5p8s2(out)
      out = self.bntr5(out)
      out = self.relu(out)
  
      out = ME.cat(out, out_b2p4)
      out = self.block6(out)
  
      # tensor_stride=2
      out = self.convtr6p4s2(out)
      out = self.bntr6(out)
      out = self.relu(out)
  
      out = ME.cat(out, out_b1p2)
      out = self.block7(out)
  
      # tensor_stride=1
      out = self.convtr7p2s2(out)
      out = self.bntr7(out)
      out = self.relu(out)
  
      out = ME.cat(out, out_p1)
      out = self.block8(out)
  
      return self.final(out)
  
imports: import torch.nn
  import MinkowskiEngine
  from .modules import BasicBlock, Bottleneck
label_desc: Use image node_id 3 for calling the MinkUNetBase obj's underlying member method code with example usage: obj.forward(x) and returns: self