code: def test_infer_dim_3(setup):
      n, p = 100, 5
      rng = np.random.RandomState(0)
      X = mt.tensor(rng.randn(n, p) * 0.1)
      X[:10] += mt.array([3, 4, 5, 1, 2])
      X[10:20] += mt.array([6, 0, 7, 2, -1])
      X[30:40] += 2 * mt.array([-1, 1, -1, 1, -1])
      pca = PCA(n_components=p, svd_solver="full")
      pca.fit(X)
      spect = pca.explained_variance_.fetch()
      assert _infer_dimension(spect, n) > 2
  
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 17 for calling a global function with example usage: test_infer_dim_3(setup) without return types