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 and predict labels for sequences in X.
  
  Convenience method to produce in-sample predictions and
  cross-validated out-of-sample predictions.
  
  Writes to self, if change_state=True:
      Sets self.is_fitted to True.
      Sets fitted model attributes ending in "_".
  
  Does not update state if change_state=False.
  
  Parameters
  ----------
  X : sktime compatible time series panel data container, Panel scitype, e.g.,
      pd-multiindex: pd.DataFrame with columns = variables,
      index = pd.MultiIndex with first level = instance indices,
      second level = time indices
      numpy3D: 3D np.array (any number of dimensions, equal length series)
      of shape [n_instances, n_dimensions, series_length]
      or of any other supported Panel mtype
      for list of mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  y : sktime compatible tabular data container, Table scitype
      1D iterable, of shape [n_instances]
      or 2D iterable, of shape [n_instances, n_dimensions]
      class labels for fitting
      0-th indices correspond to instance indices in X
      1-st indices (if applicable) correspond to multioutput vector indices in X
      supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
  cv : None, int, or sklearn cross-validation object, optional, default=None
      None : predictions are in-sample, equivalent to fit(X, y).predict(X)
      cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)
          where multiple X_train, y_train, X_test are obtained from cv folds
          returned y is union over all test fold predictions
          cv test folds must be non-intersecting
      int : equivalent to cv=KFold(cv, shuffle=True, random_state=x),
          i.e., k-fold cross-validation predictions out-of-sample
          random_state x is taken from self if exists, otherwise x=None
  change_state : bool, optional (default=True)
      if False, will not change the state of the classifier,
          i.e., fit/predict sequence is run with a copy, self does not change
      if True, will fit self to the full X and y,
          end state will be equivalent to running fit(X, y)
  
  Returns
  -------
  y_pred : sktime compatible tabular data container, Table scitype
      1D iterable, of shape [n_instances]
      or 2D iterable, of shape [n_instances, n_dimensions]
      predicted class labels
      0-th indices correspond to instance indices in X
      1-st indices (if applicable) correspond to multioutput vector indices in X
      1D np.npdarray, if y univariate (one dimension)
      otherwise, same type as y passed in fit
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code: def fit_predict(self, X, y, cv=None, change_state=True):
      """Fit and predict labels for sequences in X.
  
      Convenience method to produce in-sample predictions and
      cross-validated out-of-sample predictions.
  
      Writes to self, if change_state=True:
          Sets self.is_fitted to True.
          Sets fitted model attributes ending in "_".
  
      Does not update state if change_state=False.
  
      Parameters
      ----------
      X : sktime compatible time series panel data container, Panel scitype, e.g.,
          pd-multiindex: pd.DataFrame with columns = variables,
          index = pd.MultiIndex with first level = instance indices,
          second level = time indices
          numpy3D: 3D np.array (any number of dimensions, equal length series)
          of shape [n_instances, n_dimensions, series_length]
          or of any other supported Panel mtype
          for list of mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
      y : sktime compatible tabular data container, Table scitype
          1D iterable, of shape [n_instances]
          or 2D iterable, of shape [n_instances, n_dimensions]
          class labels for fitting
          0-th indices correspond to instance indices in X
          1-st indices (if applicable) correspond to multioutput vector indices in X
          supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
      cv : None, int, or sklearn cross-validation object, optional, default=None
          None : predictions are in-sample, equivalent to fit(X, y).predict(X)
          cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)
              where multiple X_train, y_train, X_test are obtained from cv folds
              returned y is union over all test fold predictions
              cv test folds must be non-intersecting
          int : equivalent to cv=KFold(cv, shuffle=True, random_state=x),
              i.e., k-fold cross-validation predictions out-of-sample
              random_state x is taken from self if exists, otherwise x=None
      change_state : bool, optional (default=True)
          if False, will not change the state of the classifier,
              i.e., fit/predict sequence is run with a copy, self does not change
          if True, will fit self to the full X and y,
              end state will be equivalent to running fit(X, y)
  
      Returns
      -------
      y_pred : sktime compatible tabular data container, Table scitype
          1D iterable, of shape [n_instances]
          or 2D iterable, of shape [n_instances, n_dimensions]
          predicted class labels
          0-th indices correspond to instance indices in X
          1-st indices (if applicable) correspond to multioutput vector indices in X
          1D np.npdarray, if y univariate (one dimension)
          otherwise, same type as y passed in fit
      """
      return self._fit_predict_boilerplate(
          X=X, y=y, cv=cv, change_state=change_state, method="predict"
      )
  
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 6 for calling the BaseClassifier obj's underlying member method code with example usage: obj.fit_predict(X, y, cv, change_state) and returns: self