code: def test_pca_dim():
      # Check automated dimensionality setting
      rng = np.random.RandomState(0)
      n, p = 100, 5
      X = rng.randn(n, p) * 0.1
      X[:10] += np.array([3, 4, 5, 1, 2])
      pca = dd.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 dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import scipy
  import sklearn.decomposition
  from dask.array.utils import assert_eq
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_raises
  from sklearn import datasets
  from sklearn.utils import check_random_state
  import dask_ml.decomposition
  from dask_ml._compat import DASK_2_26_0
  from dask_ml.utils import assert_estimator_equal, flip_vector_signs, svd_flip
label_desc: Use image node_id 16 for calling a global function with example usage: test_pca_dim() without return types