code: def test_compare_mifid(equal_size):
      """Check that our implementation of MIFID is correct by comparing it to the original implementation."""
      metric = MemorizationInformedFrechetInceptionDistance(
          feature=768
      ).cuda()
  
      n, m = 100, 100 if equal_size else 90
  
      # Generate some synthetic data
      torch.manual_seed(42)
      img1 = torch.randint(0, 180, (n, 3, 299, 299), dtype=torch.uint8)
      img2 = torch.randint(
          100, 255, (m, 3, 299, 299), dtype=torch.uint8
      )
  
      batch_size = 10
      for i in range(n // batch_size):
          metric.update(
              img1[batch_size * i : batch_size * (i + 1)].cuda(),
              real=True,
          )
  
      for i in range(m // batch_size):
          metric.update(
              img2[batch_size * i : batch_size * (i + 1)].cuda(),
              real=False,
          )
  
      compare_val = _compare_mifid(img1, img2)
      tm_res = metric.compute()
  
      assert torch.allclose(
          tm_res.cpu(),
          torch.tensor(compare_val, dtype=tm_res.dtype),
          atol=1e-3,
      )
  
file_path: torchmetrics/tests/unittests/image/test_mifid.py
imports: from contextlib import nullcontext
  from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.linalg import sqrtm
  from torchmetrics.image.mifid import MemorizationInformedFrechetInceptionDistance, NoTrainInceptionV3
  from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
label_desc: Use image node_id 5 for calling a global function with example usage: test_compare_mifid(equal_size) without return types