code: def test_fit_mle_too_few_samples():
      # Tests that an error is raised when the number of samples is smaller
      # than the number of features during an mle fit
      X, _ = datasets.make_classification(
          n_samples=20, n_features=21, random_state=42
      )
  
      pca = PCA(n_components="mle", svd_solver="full")
      with pytest.raises(
          ValueError,
          match="n_components='mle' is only supported if n_samples >= n_features",
      ):
          pca.fit(X)
  
imports: import re
  import warnings
  import numpy
  import pytest
  import scipy
  from numpy.testing import assert_array_equal
  from sklearn import config_context, datasets
  from sklearn.base import clone
  from sklearn.datasets import load_iris, make_classification
  from sklearn.decomposition import PCA
  from sklearn.decomposition._pca import _assess_dimension, _infer_dimension
  from sklearn.utils._array_api import _atol_for_type, _convert_to_numpy, yield_namespace_device_dtype_combinations
  from sklearn.utils._array_api import device
  from sklearn.utils._testing import _array_api_for_tests, assert_allclose
  from sklearn.utils.estimator_checks import _get_check_estimator_ids, check_array_api_input_and_values
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 38 for calling a global function with example usage: test_fit_mle_too_few_samples() without return types