code: def test_mle_redundant_data():
      # Test 'mle' with pathological X: only one relevant feature should give a
      # rank of 1
      X, _ = datasets.make_classification(
          n_features=20,
          n_informative=1,
          n_repeated=18,
          n_redundant=1,
          n_clusters_per_class=1,
          random_state=42,
      )
      pca = PCA(n_components="mle").fit(X)
      assert pca.n_components_ == 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 37 for calling a global function with example usage: test_mle_redundant_data() without return types