class_docstr: Abstract base class for time series clusterer.
  
  Parameters
  ----------
  n_clusters: int, defaults = None
      Number of clusters for model.
class_docstr_tok: ["Abstract","base","class","for","time","series","clusterer",".","Parameters","--","--","--","--","--","n_clusters",":","int",",","defaults","=","None","Number","of","clusters","for","model","."]
docstr: Fit time series clusterer to training data.
  
  Parameters
  ----------
  X : np.ndarray (2d or 3d array of shape (n_instances, series_length) or shape
      (n_instances,n_dimensions,series_length)) or pd.Dataframe.
      Training time series instances to cluster. If data is not equal length a
      pd.Dataframe given, if another other type of data a np.ndarray given.
  
  Returns
  -------
  self:
      Fitted estimator.
docstr_tok: ["Fit","time","series","clusterer","to","training","data",".","Parameters","--","--","--","--","--","X",":","np.ndarray","(","2d","or","3d","array","of","shape","(","n_instances",",","series_length",")","or","shape","(","n_instances",",","n_dimensions",",","series_length",")",")","or","pd.Dataframe",".","Training","time","series","instances","to","cluster",".","If","data","is","not","equal","length","a","pd.Dataframe","given",",","if","another","other","type","of","data","a","np.ndarray","given",".","Returns","--","--","--","-","self",":","Fitted","estimator","."]
code: def _fit(self, X: TimeSeriesInstances, y=None) -> np.ndarray:
      """Fit time series clusterer to training data.
  
      Parameters
      ----------
      X : np.ndarray (2d or 3d array of shape (n_instances, series_length) or shape
          (n_instances,n_dimensions,series_length)) or pd.Dataframe.
          Training time series instances to cluster. If data is not equal length a
          pd.Dataframe given, if another other type of data a np.ndarray given.
  
      Returns
      -------
      self:
          Fitted estimator.
      """
      raise NotImplementedError
  
imports: import time
  from typing import Any, Union
  import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import check_is_scitype, convert_to, scitype_to_mtype
  from sktime.utils.sklearn import is_sklearn_transformer
  from sktime.utils.validation import check_n_jobs
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 11 for calling the BaseClusterer obj's underlying member method code with example usage: obj._fit(X, y) without return types