code: def generate_noisy_data():
      blobs, _ = datasets.make_blobs(
          n_samples=200,
          centers=[(-0.75, 2.25), (1.0, 2.0)],
          cluster_std=0.25,
          random_state=42,
      )
      moons, _ = datasets.make_moons(
          n_samples=200, noise=0.05, random_state=42
      )
      rng = np.random.default_rng(seed=42)
      noise = rng.uniform(-1.0, 3.0, (50, 2))
  
      return np.vstack([blobs, moons, noise])
  
imports: import numpy
  from scipy.spatial import distance
  from scipy import sparse
  from scipy import stats
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.utils._testing import assert_array_equal, assert_array_almost_equal, assert_raises
  from hdbscan import HDBSCAN, hdbscan, validity_index, approximate_predict, approximate_predict_scores, membership_vector, all_points_membership_vectors
  from sklearn.datasets import make_blobs
  from sklearn.utils import shuffle
  from sklearn.preprocessing import StandardScaler
  from scipy.stats import mode
  from tempfile import mkdtemp
  from functools import wraps
  import pytest
  from sklearn import datasets
  import warnings
label_desc: Use image node_id 5 for calling a global function with example usage: generate_noisy_data() and returns: np