code: def forward(self, x):
      out_s1 = self.conv1(x)
      out_s1 = self.norm1(out_s1)
      out_s1 = self.block1(out_s1)
      out = MEF.relu(out_s1)
  
      out_s2 = self.conv2(out)
      out_s2 = self.norm2(out_s2)
      out_s2 = self.block2(out_s2)
      out = MEF.relu(out_s2)
  
      out_s4 = self.conv3(out)
      out_s4 = self.norm3(out_s4)
      out_s4 = self.block3(out_s4)
      out = MEF.relu(out_s4)
  
      out_s8 = self.conv4(out)
      out_s8 = self.norm4(out_s8)
      out_s8 = self.block4(out_s8)
      out = MEF.relu(out_s8)
  
      out = self.conv4_tr(out)
      out = self.norm4_tr(out)
      out = self.block4_tr(out)
      out_s4_tr = MEF.relu(out)
  
      out = ME.cat(out_s4_tr, out_s4)
  
      out = self.conv3_tr(out)
      out = self.norm3_tr(out)
      out = self.block3_tr(out)
      out_s2_tr = MEF.relu(out)
  
      out = ME.cat(out_s2_tr, out_s2)
  
      out = self.conv2_tr(out)
      out = self.norm2_tr(out)
      out = self.block2_tr(out)
      out_s1_tr = MEF.relu(out)
  
      out = ME.cat(out_s1_tr, out_s1)
      out = self.conv1_tr(out)
      out = MEF.relu(out)
      out = self.final(out)
  
      if self.normalize_feature:
          return ME.SparseTensor(
              out.F / torch.norm(out.F, p=2, dim=1, keepdim=True),
              coordinate_map_key=out.coordinate_map_key,
              coordinate_manager=out.coordinate_manager,
          )
      else:
          return out
  
imports: import torch
  import MinkowskiEngine
  import MinkowskiEngine.MinkowskiFunctional
  from .common import get_norm
  from .res16unet import get_block
  from .common import NormType
label_desc: Use image node_id 2 for calling the ResUNet2 obj's underlying member method code with example usage: obj.forward(x) and returns: ME, out