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):
      out16 = self.in_tr(x)  # dropout cause a lot align problem
      out32 = self.down_tr32(out16)  # [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]
      d1 = self.out_tr256(out)
      d1 = F.interpolate(
          d1,
          size=paddle.shape(x)[2:],
          data_format="NCDHW",
          mode="trilinear",
      )
      out = self.up_tr128(out, out64)  # [4, 128, 128, 128, 8]
      d2 = self.out_tr128(out)
      d2 = F.interpolate(
          d2,
          size=paddle.shape(x)[2:],
          data_format="NCDHW",
          mode="trilinear",
      )
      out = self.up_tr64(out, out32)  # [4, 64, 256, 256, 9]
      d3 = self.out_tr64(out)
      d3 = F.interpolate(
          d3,
          size=paddle.shape(x)[2:],
          data_format="NCDHW",
          mode="trilinear",
      )
      out = self.up_tr32(out, out16)  # [4, 32, 512, 512, 12]
      out = self.out_tr32(out)
  
      return [out, d1, d2, d3]
  
imports: import os
  import sys
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
  from medicalseg.utils import utils
label_desc: Use image node_id 3 for calling the VNetDeepSup obj's underlying member method code with example usage: obj.forward(x) and returns: list