code: def forward(self, x):
      #  Level 1 context pathway
      out = self.conv3d_c1_1(x)
      residual_1 = out
      out = self.lrelu(out)
      out = self.conv3d_c1_2(out)
      out = self.dropout3d(out)
      out = self.lrelu_conv_c1(out)
      # Element Wise Summation
      out += residual_1
      context_1 = self.lrelu(out)
      out = self.inorm3d_c1(out)
      out = self.lrelu(out)
  
      # Level 2 context pathway
      out = self.conv3d_c2(out)
      residual_2 = out
      out = self.norm_lrelu_conv_c2(out)
      out = self.dropout3d(out)
      out = self.norm_lrelu_conv_c2(out)
      out += residual_2
      out = self.inorm3d_c2(out)
      out = self.lrelu(out)
      context_2 = out
  
      # Level 3 context pathway
      out = self.conv3d_c3(out)
      residual_3 = out
      out = self.norm_lrelu_conv_c3(out)
      out = self.dropout3d(out)
      out = self.norm_lrelu_conv_c3(out)
      out += residual_3
      out = self.inorm3d_c3(out)
      out = self.lrelu(out)
      context_3 = out
  
      # Level 4 context pathway
      out = self.conv3d_c4(out)
      residual_4 = out
      out = self.norm_lrelu_conv_c4(out)
      out = self.dropout3d(out)
      out = self.norm_lrelu_conv_c4(out)
      out += residual_4
      out = self.inorm3d_c4(out)
      out = self.lrelu(out)
      context_4 = out
  
      # Level 5
      out = self.conv3d_c5(out)
      residual_5 = out
      out = self.norm_lrelu_conv_c5(out)
      out = self.dropout3d(out)
      out = self.norm_lrelu_conv_c5(out)
      out += residual_5
      out = self.norm_lrelu_upscale_conv_norm_lrelu_l0(out)
  
      out = self.conv3d_l0(out)
      out = self.inorm3d_l0(out)
      out = self.lrelu(out)
  
      # Level 1 localization pathway
      out = torch.cat([out, context_4], dim=1)
      out = self.conv_norm_lrelu_l1(out)
      out = self.conv3d_l1(out)
      out = self.norm_lrelu_upscale_conv_norm_lrelu_l1(out)
  
      # Level 2 localization pathway
      out = torch.cat([out, context_3], dim=1)
      out = self.conv_norm_lrelu_l2(out)
      ds2 = out
      out = self.conv3d_l2(out)
      out = self.norm_lrelu_upscale_conv_norm_lrelu_l2(out)
  
      # Level 3 localization pathway
      out = torch.cat([out, context_2], dim=1)
      out = self.conv_norm_lrelu_l3(out)
      ds3 = out
      out = self.conv3d_l3(out)
      out = self.norm_lrelu_upscale_conv_norm_lrelu_l3(out)
  
      # Level 4 localization pathway
      out = torch.cat([out, context_1], dim=1)
      out = self.conv_norm_lrelu_l4(out)
      out_pred = self.conv3d_l4(out)
  
      ds2_1x1_conv = self.ds2_1x1_conv3d(ds2)
      ds1_ds2_sum_upscale = self.upsacle(ds2_1x1_conv)
      ds3_1x1_conv = self.ds3_1x1_conv3d(ds3)
      ds1_ds2_sum_upscale_ds3_sum = ds1_ds2_sum_upscale + ds3_1x1_conv
      ds1_ds2_sum_upscale_ds3_sum_upscale = self.upsacle(
          ds1_ds2_sum_upscale_ds3_sum
      )
  
      out = out_pred + ds1_ds2_sum_upscale_ds3_sum_upscale
      seg_layer = out
      out = (
          out.permute(0, 2, 3, 4, 1)
          .contiguous()
          .view(-1, self.n_classes)
      )
      out = self.softmax(out)
      return F.sigmoid(seg_layer)
  
file_path: NiftyNet/demos/PyTorchNiftyNet/libs/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 6 for calling the Modified3DUNet obj's underlying member method code with example usage: obj.forward(x) and returns: F