code: def test_mcd_support_covariance_is_zero():
      # Check that MCD returns a ValueError with informative message when the
      # covariance of the support data is equal to 0.
      X_1 = np.array(
          [0.5, 0.1, 0.1, 0.1, 0.957, 0.1, 0.1, 0.1, 0.4285, 0.1]
      )
      X_1 = X_1.reshape(-1, 1)
      X_2 = np.array(
          [0.5, 0.3, 0.3, 0.3, 0.957, 0.3, 0.3, 0.3, 0.4285, 0.3]
      )
      X_2 = X_2.reshape(-1, 1)
      msg = (
          "The covariance matrix of the support data is equal to 0, try to "
          "increase support_fraction"
      )
      for X in [X_1, X_2]:
          with pytest.raises(ValueError, match=msg):
              MinCovDet().fit(X)
  
file_path: scikit-learn/sklearn/covariance/tests/test_robust_covariance.py
imports: import itertools
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.covariance import MinCovDet, empirical_covariance, fast_mcd
  from sklearn.utils._testing import assert_array_almost_equal
label_desc: Use image node_id 7 for calling a global function with example usage: test_mcd_support_covariance_is_zero() without return types