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.
code: def __init__(self):
      # reserved attributes written to in fit
      self.classes_ = []  # classes seen in y, unique labels
      self.n_classes_ = 0  # number of unique classes in y
      self.fit_time_ = 0  # time elapsed in last fit call
      self._class_dictionary = {}
      self._threads_to_use = 1
      self._X_metadata = []  # metadata/properties of X seen in fit
  
      # required for compatibility with some sklearn interfaces
      # i.e. CalibratedClassifierCV
      self._estimator_type = "classifier"
      self._is_vectorized = False
      self._converter_store_y = {}
  
      super().__init__()
      _check_estimator_deps(self)
  
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 1 to create a new BaseClassifier object from inherited base classes: BasePanelMixin with example: obj = BaseClassifier()