code: def test_approx_predict_default():
      """
      Verify that approximate_predict_flat produces same results as default
      """
      # Given the base HDBSCAN trained on some data,
      clusterer = HDBSCAN(
          cluster_selection_method="eom",
          cluster_selection_epsilon=0,
          prediction_data=True,
      ).fit(X)
  
      # When using approximate_predict_flat without specifying n_clusters,
      labels_flat, proba_flat = approximate_predict_flat(
          clusterer, X_test, n_clusters=None
      )
  
      # Then, the clustering should match that due to approximate_predict,
      labels_base, proba_base = approximate_predict(clusterer, X_test)
      assert_array_equal(labels_flat, labels_base)
      assert_array_equal(proba_flat, proba_base)
      return
  
file_path: hdbscan/hdbscan/tests/test_flat.py
imports: import warnings
  import numpy
  from hdbscan import HDBSCAN, approximate_predict
  from hdbscan.flat import HDBSCAN_flat, approximate_predict_flat, membership_vector_flat, all_points_membership_vectors_flat
  from sklearn.datasets import make_blobs, make_moons
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.utils._testing import assert_array_equal, assert_array_less
label_desc: Use image node_id 5 for calling a global function with example usage: test_approx_predict_default() without return types