code: def get_gaussian_centers(y, n_components):
      if isinstance(y, Series) or isinstance(y, DataFrame):
          y = y.values
      if y.ndim == 1:
          y = y.reshape(-1, 1)
      cluster = KMeans(n_clusters=n_components, random_state=42).fit(y)
      return cluster.cluster_centers_.ravel().tolist()
  
file_path: pytorch_tabular/src/pytorch_tabular/utils/data_utils.py
imports: import gzip
  import os
  import random
  import shutil
  import numpy
  import pandas
  import requests
  import torch
  from pandas import DataFrame, Series
  from sklearn.cluster import KMeans
  from sklearn.datasets import make_classification, make_regression
  from sklearn.preprocessing import LabelEncoder
  from .logger import get_logger
label_desc: Use image node_id 4 for calling a global function with example usage: get_gaussian_centers(y, n_components) and returns: cluster