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 test(self):
      import numpy as np
  
      np.random.seed(1)
      a = np.random.rand(1, self.in_channels, 32, 32, 32)
      input_tensor = paddle.to_tensor(a, dtype="float32")
  
      ideal_out = paddle.rand((1, self.num_classes, 32, 32, 32))
      out = self.forward(input_tensor)[0]
      print("out", out.mean(), input_tensor.mean())
  
      assert ideal_out.shape == out.shape
      paddle.summary(self, (1, self.in_channels, 32, 32, 32))
  
      print("Vnet test is complete")
  
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 4 for calling the VNet obj's underlying member method code with example usage: obj.test() without return types