code: def test_forward():
      patcher = Patcher(stride=3)
      x = torch.arange(2 * 3 * 5 * 7).view(2, 3, 5, 7).to(torch.float32)
      # apply the forward method to the input tensor
      patched = patcher(x, patch_size=5)
      assert patched.num_patches == (2, 3)
      # First dim is batch * total number of patches (2*3)
      # Second dim is seq len = patch_size * patch_size, last dim is C
      assert patched.tensor.shape == (2 * 2 * 3, 5 * 5, 3)
  
      # patch_size == stride, then unpached should equal padded input
      ps = 3
      patched = patcher(x, patch_size=ps)
      padded_input, *_ = patcher._pad(x, patch_size=ps)
      unpatched = patcher.unpatch(patched, channels_last=True)
      assert (
          padded_input.shape == unpatched.shape
      ), f"{unpatched.shape}, {padded_input.shape}"
      assert (unpatched == padded_input).all()
  
imports: import pytest
  import torch
  from projects.torch_vct.neural.patcher import Patcher
label_desc: Use image node_id 2 for calling a global function with example usage: test_forward() without return types