class_docstr: Abstract class that implements time series Lloyds algorithm.
  
  Parameters
  ----------
  n_clusters: int, defaults = 8
      The number of clusters to form as well as the number of
      centroids to generate.
  init_algorithm: str, np.ndarray (3d array of shape (n_clusters, n_dimensions,
      series_length)), defaults = 'forgy'
      Method for initializing cluster centers or an array of initial cluster centers.
      Any of the following strings are valid: ['kmeans++', 'random', 'forgy']
  metric: str or Callable, defaults = 'dtw'
      Distance metric to compute similarity between time series. Any of the following
      are valid: ['dtw', 'euclidean', 'erp', 'edr', 'lcss', 'squared', 'ddtw', 'wdtw',
      'wddtw']
  n_init: int, defaults = 10
      Number of times the k-means algorithm will be run with different
      centroid seeds. The final result will be the best output of n_init
      consecutive runs in terms of inertia.
  max_iter: int, defaults = 30
      Maximum number of iterations of the k-means algorithm for a single
      run.
  tol: float, defaults = 1e-6
      Relative tolerance with regards to Frobenius norm of the difference
      in the cluster centers of two consecutive iterations to declare
      convergence.
  verbose: bool, defaults = False
      Verbosity mode.
  random_state: int or np.random.RandomState instance or None, defaults = None
      Determines random number generation for centroid initialization.
  distance_params: dict, defaults = None
      Dictionary containing kwargs for the distance metric being used.
  
  Attributes
  ----------
  cluster_centers_: np.ndarray (3d array of shape (n_clusters, n_dimensions,
      series_length))
      Time series that represent each of the cluster centers. If the algorithm stops
      before fully converging these will not be consistent with labels_.
  labels_: np.ndarray (1d array of shape (n_instance,))
      Labels that is the index each time series belongs to.
  inertia_: float
      Sum of squared distances of samples to their closest cluster center, weighted by
      the sample weights if provided.
  n_iter_: int
      Number of iterations run.
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code: def _score(self, X, y=None):
      return -self.inertia_
  
imports: from abc import ABC, abstractmethod
  from typing import Callable, Tuple, Union
  import numpy
  from numpy.random import RandomState
  from sklearn.utils import check_random_state
  from sklearn.utils.extmath import stable_cumsum
  from sktime.clustering.base import BaseClusterer
  from sktime.clustering.metrics.averaging import mean_average
  from sktime.distances import distance_factory, pairwise_distance
  from sktime.distances._ddtw import average_of_slope_transform
label_desc: Use image node_id 7 for calling the TimeSeriesLloyds obj's underlying member method code with example usage: obj._score(X, y) and returns: unknown