class_docstr: Abstract base class for time series regressors.
  
  The base regressor specifies the methods and method signatures that all
  regressors have to implement. Attributes with a underscore suffix are set in the
  method fit.
  
  Parameters
  ----------
  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.
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docstr: Fit time series regressor to training data.
  
  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
      if self.get_tag("X_inner_mtype") = "nested_univ":
      pd.DataFrame with each column a dimension, each cell a pd.Series
      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.
  
  Notes
  -----
  Changes state by creating a fitted model that updates attributes ending in "_"
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code: def _fit(self, X, y):
      """Fit time series regressor to training data.
  
      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
          if self.get_tag("X_inner_mtype") = "nested_univ":
          pd.DataFrame with each column a dimension, each cell a pd.Series
          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.
  
      Notes
      -----
      Changes state by creating a fitted model that updates attributes ending in "_"
      """
      raise NotImplementedError("abstract method")
  
file_path: sktime/sktime/regression/base.py
imports: import time
  import numpy
  from sktime.base import BasePanelMixin
  from sktime.datatypes import VectorizedDF
  from sktime.utils.sklearn import is_sklearn_transformer
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 6 for calling the BaseRegressor obj's underlying member method code with example usage: obj._fit(X, y) without return types