code: def test_mcd(global_random_seed):
      # Tests the FastMCD algorithm implementation
      # Small data set
      # test without outliers (random independent normal data)
      launch_mcd_on_dataset(
          100, 5, 0, 0.02, 0.1, 75, global_random_seed
      )
      # test with a contaminated data set (medium contamination)
      launch_mcd_on_dataset(
          100, 5, 20, 0.3, 0.3, 65, global_random_seed
      )
      # test with a contaminated data set (strong contamination)
      launch_mcd_on_dataset(
          100, 5, 40, 0.1, 0.1, 50, global_random_seed
      )
  
      # Medium data set
      launch_mcd_on_dataset(
          1000, 5, 450, 0.1, 0.1, 540, global_random_seed
      )
  
      # Large data set
      launch_mcd_on_dataset(
          1700, 5, 800, 0.1, 0.1, 870, global_random_seed
      )
  
      # 1D data set
      launch_mcd_on_dataset(
          500, 1, 100, 0.02, 0.02, 350, global_random_seed
      )
  
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 1 for calling a global function with example usage: test_mcd(global_random_seed) without return types