code: def make_var_density_blobs(
      n_samples=750, centers=[[0, 0]], cluster_std=[0.5], random_state=0
  ):
      samples_per_blob = n_samples // len(centers)
      blobs = [
          make_blobs(
              n_samples=samples_per_blob,
              centers=[c],
              cluster_std=cluster_std[i],
          )[0]
          for i, c in enumerate(centers)
      ]
      labels = [
          i * np.ones(samples_per_blob) for i in range(len(centers))
      ]
      return np.vstack(blobs), np.hstack(labels)
  
file_path: hdbscan/examples/plot_hdbscan.py
imports: import numpy
  from hdbscan import HDBSCAN
  from sklearn.cluster import DBSCAN
  from sklearn import metrics
  from sklearn.datasets.samples_generator import make_blobs
  from sklearn.preprocessing import StandardScaler
  import time
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling a global function with example usage: make_var_density_blobs(n_samples, centers, cluster_std, random_state) and returns: np,  np