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: Format data as valid panel mtype of the data.
  
  Parameters
  ----------
  X: Any
      Data to convert to panel mtype.
  
  Returns
  -------
  X: np.ndarray (at least 2d) or pd.Dataframe or List[pd.Dataframe]
      Converted X.
docstr_tok: ["Format","data","as","valid","panel","mtype","of","the","data",".","Parameters","--","--","--","--","--","X",":","Any","Data","to","convert","to","panel","mtype",".","Returns","--","--","--","-","X",":","np.ndarray","(","at","least","2d",")","or","pd.Dataframe","or","List","[","pd.Dataframe","]","Converted","X","."]
code: def _initial_conversion(X: Any) -> TimeSeriesInstances:
      """Format data as valid panel mtype of the data.
  
      Parameters
      ----------
      X: Any
          Data to convert to panel mtype.
  
      Returns
      -------
      X: np.ndarray (at least 2d) or pd.Dataframe or List[pd.Dataframe]
          Converted X.
      """
      if isinstance(X, np.ndarray) and X.ndim == 2:
          X = X.reshape(X.shape[0], 1, X.shape[1])
      return X
  
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 13 for calling the BaseClusterer obj's underlying member method code with example usage: obj._initial_conversion(X) and returns: X