code: def test_shape(self, spatial_dim):
      spatial_dim = int(spatial_dim)
  
      model = UNet(
          spatial_dims=2,
          in_channels=1,
          out_channels=1,
          channels=(4, 8, 16),
          strides=(2, 2),
          num_res_units=2,
      )
  
      device = "cuda:0" if torch.cuda.is_available() else "cpu"
      model.to(device)
      model.eval()
  
      # Initialize a dummy 3D tensor volume with shape (N,C,D,H,W)
      input_volume = torch.ones(1, 1, 64, 256, 256, device=device)
  
      # Remove spatial dim to slide across from the roi_size
      roi_size = list(input_volume.shape[2:])
      roi_size.pop(spatial_dim)
  
      # Initialize and run inferer
      inferer = SliceInferer(
          roi_size=roi_size,
          spatial_dim=spatial_dim,
          sw_batch_size=1,
          cval=-1,
      )
      result = inferer(input_volume, model)
  
      self.assertTupleEqual(result.shape, input_volume.shape)
  
      # test that the inferer can be run multiple times
      result = inferer(input_volume, model)
  
file_path: MONAI/tests/test_slice_inferer.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.inferers import SliceInferer
  from monai.networks.nets import UNet
label_desc: Use image node_id 1 for calling the TestSliceInferer obj's underlying member method code with example usage: obj.test_shape(spatial_dim) without return types