code: def test_mle_simple_case():
      # non-regression test for issue
      # https://github.com/scikit-learn/scikit-learn/issues/16730
      n_samples, n_dim = 1000, 10
      X = np.random.RandomState(0).randn(n_samples, n_dim)
      X[:, -1] = np.mean(X[:, :-1], axis=-1)  # true X dim is ndim - 1
      pca_skl = PCA("mle", svd_solver="full")
      pca_skl.fit(X)
      assert pca_skl.n_components_ == n_dim - 1
  
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 39 for calling a global function with example usage: test_mle_simple_case() without return types