code: def test_interpolation_methods(interpolation_method):
      """Test that interpolation method works as expected."""
      latent1 = torch.randn(100, 25)
      latent2 = torch.randn(100, 25)
  
      res1 = _interpolate(latent1, latent2, 1e-4, interpolation_method)
      res2 = batch_interp(latent1, latent2, 1e-4, interpolation_method)
      assert torch.allclose(res1, res2)
  
imports: from operator import attrgetter
  import pytest
  import torch
  import torch_fidelity
  from torch import nn
  from torch_fidelity.sample_similarity_lpips import SampleSimilarityLPIPS
  from torch_fidelity.utils import batch_interp
  from torchmetrics.functional.image.lpips import _LPIPS
  from torchmetrics.functional.image.perceptual_path_length import _interpolate, perceptual_path_length
  from torchmetrics.image.perceptual_path_length import PerceptualPathLength
  from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
  from unittests import skip_on_running_out_of_memory
  from unittests.helpers import seed_all
label_desc: Use image node_id 1 for calling a global function with example usage: test_interpolation_methods(interpolation_method) without return types