code: def test_no_empty_slice_warning():
      # test if we avoid numpy warnings for computing over empty arrays
      n_components = 10
      n_features = (
          n_components + 2
      )  # anything > n_comps triggered it in 0.16
      X = np.random.uniform(-1, 1, size=(n_components, n_features))
      pca = PCA(n_components=n_components)
      with warnings.catch_warnings():
          warnings.simplefilter("error", RuntimeWarning)
          pca.fit(X)
  
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 6 for calling a global function with example usage: test_no_empty_slice_warning() without return types