code: def test_fid_same_input(feature):
      """If real and fake are update on the same data the fid score should be 0."""
      metric = MemorizationInformedFrechetInceptionDistance(
          feature=feature
      )
  
      for _ in range(2):
          img = torch.randint(
              0, 255, (10, 3, 299, 299), dtype=torch.uint8
          )
          metric.update(img, real=True)
          metric.update(img, real=False)
  
      assert torch.allclose(
          torch.cat(metric.real_features, dim=0),
          torch.cat(metric.fake_features, dim=0),
      )
  
      val = metric.compute()
      assert torch.allclose(val, torch.zeros_like(val), 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 4 for calling a global function with example usage: test_fid_same_input(feature) without return types