code: def test_infer_dim_1():
      # TODO: explain what this is testing
      # Or at least use explicit variable names...
      n, p = 1000, 5
      rng = np.random.RandomState(0)
      X = (
          rng.randn(n, p) * 0.1
          + rng.randn(n, 1) * np.array([3, 4, 5, 1, 2])
          + np.array([1, 0, 7, 4, 6])
      )
      pca = PCA(n_components=p, svd_solver="full")
      pca.fit(X)
      spect = pca.explained_variance_
      ll = np.array(
          [_assess_dimension(spect, k, n) for k in range(1, p)]
      )
      assert ll[1] > ll.max() - 0.01 * n
  
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 20 for calling a global function with example usage: test_infer_dim_1() without return types