code: def _estimate_dummy_outcome(
      action: str, X_train: np.ndarray, outcome: np.ndarray, **func_args
  ):
      """
      A function that takes in any sklearn estimator and returns a trained estimator
  
      :param 'action': str
          The sklearn estimator to be used.
      :param 'X_train': np.ndarray
          The variable used to estimate the value of outcome.
      :param 'outcome': np.ndarray
          The variable which we wish to estimate.
      :param 'func_args': variable length keyworded argument
          The parameters passed to the estimator.
      """
      estimator = _get_regressor_object(action, **func_args)
      X = X_train
      y = outcome
  
      estimator = estimator.fit(X, y)
  
      return estimator.predict
  
imports: import copy
  import logging
  from collections import OrderedDict, namedtuple
  from typing import Callable, List, Optional, Union
  import numpy
  import pandas
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.neural_network import MLPRegressor
  from sklearn.svm import SVR
  from tqdm.auto import tqdm
  from dowhy.causal_estimator import CausalEstimate
  from dowhy.causal_estimators.econml import Econml
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.causal_refuter import CausalRefutation, CausalRefuter, choose_variables, test_significance
label_desc: Use image node_id 5 for calling a global function with example usage: _estimate_dummy_outcome(action, X_train, outcome) and returns: estimator