class_docstr: Abstract base strategy class.
  
  Implements attributes and operations shared by all strategies, including input and
  compatibility checks between passed estimator, data and task.
docstr: Fit the strategy to the given task and data.
  
  Parameters
  ----------
  task : Task
      Task encapsulating metadata information on feature and target
      variables to which to fit the data to.
  data : pandas.DataFrame
      Dataframe with feature and target variables as specified in task.
  
  Returns
  -------
  self : an instance of the self
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code: def fit(self, task, data):
      """Fit the strategy to the given task and data.
  
      Parameters
      ----------
      task : Task
          Task encapsulating metadata information on feature and target
          variables to which to fit the data to.
      data : pandas.DataFrame
          Dataframe with feature and target variables as specified in task.
  
      Returns
      -------
      self : an instance of the self
      """
      self._validate_data(data)
  
      # Check task compatibility with strategy
      self._check_task_compatibility(task)
      self._task = task
  
      # Set metadata if not already set
      if self._task.metadata is None:
          self._task.set_metadata(data)
  
      # strategy-specific implementation
      return self._fit(data)
  
imports: import pandas
  from joblib import dump, load
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  from sklearn.pipeline import Pipeline
  from sktime.base import BaseEstimator
  from sktime.classification.base import BaseClassifier
  from sktime.forecasting.base._sktime import BaseForecaster
  from sktime.regression.base import BaseRegressor
label_desc: Use image node_id 5 for calling the BaseStrategy obj's underlying member method code with example usage: obj.fit(task, data) and returns: self