code: def test_not_enough_samples():
      """Test that an error is raised if not enough samples were provided."""
      img = torch.randint(0, 255, (1, 3, 299, 299), dtype=torch.uint8)
      metric = FrechetInceptionDistance()
      metric.update(img, real=True)
      metric.update(img, real=False)
      with pytest.raises(
          RuntimeError,
          match="More than one sample is required for both the real and fake distributed to compute FID",
      ):
          metric.compute()
  
imports: import pickle
  from contextlib import nullcontext
  from functools import partial
  import pytest
  import torch
  from torch.nn import Module
  from torch.utils.data import Dataset
  from torchmetrics.image.fid import FrechetInceptionDistance, NoTrainInceptionV3
  from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
label_desc: Use image node_id 9 for calling a global function with example usage: test_not_enough_samples() without return types