code: def test_pca_dim(setup):
      # Check automated dimensionality setting
      rng = np.random.RandomState(0)
      n, p = 100, 5
      X = mt.tensor(rng.randn(n, p) * 0.1)
      X[:10] += mt.array([3, 4, 5, 1, 2])
      pca = PCA(n_components="mle", svd_solver="full").fit(X)
      assert pca.n_components == "mle"
      assert pca.n_components_ == 1
  
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 14 for calling a global function with example usage: test_pca_dim(setup) without return types