code: def predict(self, X, **predict_params):
      check_is_fitted(self)
      # same as in ``fit``, we validate the given metadata
      request_router = get_routing_for_object(self)
      request_router.validate_metadata(
          params=predict_params, method="predict"
      )
      # and then prepare the input to the underlying ``predict`` method.
      params = request_router.route_params(
          params=predict_params, caller="predict"
      )
      return self.estimator_.predict(X, **params.estimator.predict)
  
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 4 for calling the RouterConsumerClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: self