code: def test_johnson_lindenstrauss_min_dim():
      n_tests = 10000
      n_samples = np.random.randint(low=50, high=1e10, size=n_tests)
      eps_values = np.random.rand(n_tests) + 1e-17  # range (0,1)
      tests = zip(n_samples, eps_values)
  
      for n_samples, eps in tests:
          cuml_value = cuml_johnson_lindenstrauss_min_dim(
              n_samples, eps=eps
          )
          sklearn_value = sklearn_johnson_lindenstrauss_min_dim(
              n_samples, eps=eps
          )
          assert cuml_value == sklearn_value
  
file_path: cuml/python/cuml/tests/test_random_projection.py
imports: from cuml.common import has_scipy
  from sklearn.datasets import make_blobs
  from sklearn.random_projection import johnson_lindenstrauss_min_dim
  from cuml.random_projection import johnson_lindenstrauss_min_dim
  from cuml.random_projection import GaussianRandomProjection, SparseRandomProjection
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 3 for calling a global function with example usage: test_johnson_lindenstrauss_min_dim() without return types