class_docstr: Abstract base class for time series classifiers.
  
  The base classifier specifies the methods and method signatures that all
  classifiers have to implement. Attributes with an underscore suffix are set in the
  method fit.
  
  Parameters
  ----------
  classes_            : ndarray of class labels, possibly strings
  n_classes_          : integer, number of classes (length of classes_)
  fit_time_           : integer, time (in milliseconds) for fit to run.
  _class_dictionary   : dictionary mapping classes_ onto integers 0...n_classes_-1.
  _threads_to_use     : number of threads to use in fit as determined by n_jobs.
docstr: Fit time series classifier to training data.
  
  Abstract method, must be implemented.
  
  Writes to self:
      Sets fitted model attributes ending in "_".
  
  Parameters
  ----------
  X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
      if self.get_tag("X_inner_mtype") = "numpy3D":
      3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
      if self.get_tag("X_inner_mtype") = "pd-multiindex:":
      pd.DataFrame with columns = variables,
      index = pd.MultiIndex with first level = instance indices,
      second level = time indices
      for list of other mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
      1D iterable, of shape [n_instances]
      or 2D iterable, of shape [n_instances, n_dimensions]
      class labels for fitting
      if self.get_tag("capaility:multioutput") = False, guaranteed to be 1D
      if self.get_tag("capaility:multioutput") = True, guaranteed to be 2D
  
  Returns
  -------
  self : Reference to self.
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code: def _fit(self, X, y):
      """Fit time series classifier to training data.
  
      Abstract method, must be implemented.
  
      Writes to self:
          Sets fitted model attributes ending in "_".
  
      Parameters
      ----------
      X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
          if self.get_tag("X_inner_mtype") = "numpy3D":
          3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
          if self.get_tag("X_inner_mtype") = "pd-multiindex:":
          pd.DataFrame with columns = variables,
          index = pd.MultiIndex with first level = instance indices,
          second level = time indices
          for list of other mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
      y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
          1D iterable, of shape [n_instances]
          or 2D iterable, of shape [n_instances, n_dimensions]
          class labels for fitting
          if self.get_tag("capaility:multioutput") = False, guaranteed to be 1D
          if self.get_tag("capaility:multioutput") = True, guaranteed to be 2D
  
      Returns
      -------
      self : Reference to self.
      """
      raise NotImplementedError("abstract method")
  
imports: import time
  import numpy
  from sktime.base import BasePanelMixin
  from sktime.datatypes import VectorizedDF, check_is_scitype, convert
  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 12 for calling the BaseClassifier obj's underlying member method code with example usage: obj._fit(X, y) without return types