code: def test_shape(
      self,
      coil_sens_model,
      refinement_model,
      num_cascades,
      input_shape,
      expected_shape,
  ):
      net = VariationalNetworkModel(
          coil_sens_model, refinement_model, num_cascades
      ).to(device)
      mask_shape = [1 for _ in input_shape]
      mask_shape[-2] = input_shape[-2]
      mask = torch.zeros(mask_shape)
      mask[
          ..., mask_shape[-2] // 2 - 5 : mask_shape[-2] // 2 + 5, :
      ] = 1
  
      with eval_mode(net):
          result = net(
              torch.randn(input_shape).to(device),
              mask.bool().to(device),
          )
      self.assertEqual(result.shape, expected_shape)
  
file_path: MONAI/tests/test_varnet.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.apps.reconstruction.networks.nets.coil_sensitivity_model import CoilSensitivityModel
  from monai.apps.reconstruction.networks.nets.complex_unet import ComplexUnet
  from monai.apps.reconstruction.networks.nets.varnet import VariationalNetworkModel
  from monai.networks import eval_mode
  from tests.utils import SkipIfBeforePyTorchVersion, test_script_save
label_desc: Use image node_id 1 for calling the TestVarNet obj's underlying member method code with example usage: obj.test_shape(coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape) without return types