class_docstr: Implementations based on the Vnet paper: https://arxiv.org/abs/1606.04797
class_docstr_tok: ["Implementations","based","on","the","Vnet","paper",":","https",":","\/\/arxiv.org\/abs\/1606.04797"]
code: def forward(self, x, additional_features):  # [4, 1, 512, 512, 12]
      x = self.in_tr(x)  # dropout cause a lot align problem
  
      if (
          additional_features is not None
      ):  # todo check shape [B, 16, H, W, D] # [4, 16, 512, 512, 12] #
          x = x + additional_features
  
      out32 = self.down_tr32(x)  # [4, 32, 256, 256, 9]
      out64 = self.down_tr64(out32)  # [4, 64, 128, 128, 8]
      out128 = self.down_tr128(out64)  # [4, 128, 64, 64, 4]
      out256 = self.down_tr256(out128)  # [4, 256, 32, 32, 2]
      out = self.up_tr256(out256, out128)  # [4, 256, 64, 64, 4]
      out = self.up_tr128(out, out64)  # [4, 128, 128, 128, 8]
      out = self.up_tr64(out, out32)  # [4, 64, 256, 256, 9]
      out = self.up_tr32(out, x)  # [4, 32, 512, 512, 12]
      out = self.out_tr(out)  # [4, num_classes, 512, 512, 12]
      return out
  
imports: import os
  import sys
  import paddle
  import paddle.nn
  import numpy
  from inference.ops import DistMaps3D, ScaleLayer, BatchImageNormalize3D
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the VNet obj's underlying member method code with example usage: obj.forward(x, additional_features) and returns: out