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: Predicts labels for sequences in X.
  
  Abstract method, must be implemented.
  
  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
  
  Returns
  -------
  y : should be of mtype in self.get_tag("y_inner_mtype")
      1D iterable, of shape [n_instances]
      or 2D iterable, of shape [n_instances, n_dimensions]
      predicted class labels
      indices correspond to instance indices in X
      if self.get_tag("capaility:multioutput") = False, should be 1D
      if self.get_tag("capaility:multioutput") = True, should be 2D
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code: def _predict(self, X) -> np.ndarray:
      """Predicts labels for sequences in X.
  
      Abstract method, must be implemented.
  
      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
  
      Returns
      -------
      y : should be of mtype in self.get_tag("y_inner_mtype")
          1D iterable, of shape [n_instances]
          or 2D iterable, of shape [n_instances, n_dimensions]
          predicted class labels
          indices correspond to instance indices in X
          if self.get_tag("capaility:multioutput") = False, should be 1D
          if self.get_tag("capaility:multioutput") = True, should be 2D
      """
      y_proba = self._predict_proba(X)
      y_pred = y_proba.argmax(axis=1)
  
      return y_pred
  
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 13 for calling the BaseClassifier obj's underlying member method code with example usage: obj._predict(X) and returns: y_pred