code: def test_assess_dimesion_rank_one():
      # Make sure assess_dimension works properly on a matrix of rank 1
      n_samples, n_features = 9, 6
      X = np.ones((n_samples, n_features))  # rank 1 matrix
      _, s, _ = np.linalg.svd(X, full_matrices=True)
      # except for rank 1, all eigenvalues are 0 resp. close to 0 (FP)
      assert_allclose(s[1:], np.zeros(n_features - 1), atol=1e-12)
  
      assert np.isfinite(
          _assess_dimension(s, rank=1, n_samples=n_samples)
      )
      for rank in range(2, n_features):
          assert _assess_dimension(s, rank, n_samples) == -np.inf
  
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 40 for calling a global function with example usage: test_assess_dimesion_rank_one() without return types