code: def test_infer_dim_1(setup):
      # TODO: explain what this is testing
      # Or at least use explicit variable names...
      n, p = 1000, 5
      rng = np.random.RandomState(0)
      X = (
          mt.tensor(rng.randn(n, p)) * 0.1
          + mt.tensor(rng.randn(n, 1)) * mt.array([3, 4, 5, 1, 2])
          + mt.array([1, 0, 7, 4, 6])
      )
      pca = PCA(n_components=p, svd_solver="full")
      pca.fit(X)
      spect = pca.explained_variance_.to_numpy()
      ll = np.array(
          [_assess_dimension(spect, k, n) for k in range(1, p)]
      )
      assert ll[1] > ll.max() - 0.01 * n
  
imports: from itertools import product
  import numpy
  import pytest
  import scipy
  from sklearn import datasets
  from sklearn.utils._testing import assert_array_almost_equal, assert_almost_equal, assert_raises_regex, assert_raise_message, assert_raises
  from ....None import tensor
  from .._pca import PCA, _assess_dimension, _infer_dimension
label_desc: Use image node_id 15 for calling a global function with example usage: test_infer_dim_1(setup) without return types