code: def fit(self, X, y, **fit_params):
      # meta-estimators are responsible for validating the given metadata.
      # `get_routing_for_object` is a safe way to construct a
      # `MetadataRouter` or a `MetadataRequest` from the given object.
      request_router = get_routing_for_object(self)
      request_router.validate_metadata(params=fit_params, method="fit")
      # we can use provided utility methods to map the given metadata to what
      # is required by the underlying estimator. Here `method` refers to the
      # parent's method, i.e. `fit` in this example.
      routed_params = request_router.route_params(
          params=fit_params, caller="fit"
      )
  
      # the output has a key for each object's method which is used here,
      # i.e. parent's `fit` method, containing the metadata which should be
      # routed to them, based on the information provided in
      # `get_metadata_routing`.
      self.estimator_ = clone(self.estimator).fit(
          X, y, **routed_params.estimator.fit
      )
      self.classes_ = self.estimator_.classes_
      return self
  
imports: import warnings
  from pprint import pprint
  import numpy
  from sklearn import set_config
  from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, TransformerMixin, clone
  from sklearn.linear_model import LinearRegression
  from sklearn.utils import metadata_routing
  from sklearn.utils.metadata_routing import MetadataRouter, MethodMapping, get_routing_for_object, process_routing
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 3 for calling the MetaClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self