code: def test_pad():
      # create a Patcher instance with stride 2
      patcher = Patcher(stride=2)
  
      # create a simple input tensor
      x = torch.ones(2, 3, 4, 8)
      # pad the input tensor -- there should be no padding
      x_padded, n_patches_H, n_patches_W = patcher._pad(x, patch_size=2)
      assert x_padded.shape == (2, 3, 4, 8)
      # check that the number of patches in each dimension is correct
      assert n_patches_H == 2 and n_patches_W == 4
  
      patcher = Patcher(stride=3)
      x = torch.ones(2, 3, 5, 7)
      x_padded, n_patches_H, n_patches_W = patcher._pad(x, patch_size=5)
      assert x_padded.shape == (2, 3, 8, 11)
      assert n_patches_H == 2 and n_patches_W == 3
  
      # expect valueerror if number of pads to be added is not even, i.e
      # patch_size - stride is not even
      with pytest.raises(ValueError):
          x_padded, n_patches_H, n_patches_W = patcher._pad(
              x, patch_size=4
          )
  
imports: import pytest
  import torch
  from projects.torch_vct.neural.patcher import Patcher
label_desc: Use image node_id 1 for calling a global function with example usage: test_pad() without return types