class_docstr: Container for clustering calculations and visualization.
  
  This class (object) is returned from the ``cluster`` function. The
  attributes documented below can be accessed or extracted.
  
  Attributes:
      method (str): {'kmeans', 'hdbscan'} The type of clustering algorithm
      clusters (List[int]): The predicted cluster labels
      estimator: The clustering estimator/model
      input_data: The input data
      scaled_data: The data after applying standardization
      viz_data: The data used for the default visualization i.e. reduced to 2 dimensions
      dim_method (str): The algorithm used for dimensionality reduction
      reductor: The dimensionality reduction estimator
      xlabel (str): The x-axis label for the cluster plot
      ylabel (str): The y-axis label for the cluster plot
      n_clusters (int, optional): (KMeans) The number of clusters (``k``) used in the
          final clustering fit.
      search (bool, optional): (KMeans) If True, a search was performed for optimal
          ``n_clusters``.
      cluster_range (Tuple[int, int], optional): (KMeans) The range of clusters
          searched as (min_cluster, max_cluster).
      metric (str, optional): (KMeans) The metric used to evaluate the cluster search.
      scores (List): (KMeans) The metric scores in cluster search.
code: def __str__(self):
      return "data-describe Cluster Widget"
  
imports: from typing import Optional, Tuple, List, Union
  import pandas
  import numpy
  import seaborn
  import sklearn
  from sklearn.cluster import KMeans
  import matplotlib.pyplot
  from matplotlib.ticker import MaxNLocator
  from sklearn.preprocessing import StandardScaler
  import plotly.graph_objs
  import plotly.offline
  from data_describe.config._config import get_option
  from data_describe._widget import BaseWidget
  from data_describe.compat import _is_dataframe, _compat, _requires, _in_notebook
  from data_describe.backends import _get_viz_backend, _get_compute_backend
  from data_describe.dimensionality_reduction.dimensionality_reduction import dim_reduc
label_desc: Use image node_id 2 for calling the ClusterWidget obj's underlying member method code with example usage: obj.__str__() and returns: str