code: def _get_regressor_object(action: str, **func_args):
      """
      Return a sklearn estimator object based on the estimator and corresponding parameters
  
      :param 'action': str
          The sklearn estimator used.
      :param 'func_args': variable length keyworded argument
          The parameters passed to the sklearn estimator.
      """
      if action == "linear_regression":
          return LinearRegression(**func_args)
      elif action == "knn":
          return KNeighborsRegressor(**func_args)
      elif action == "svm":
          return SVR(**func_args)
      elif action == "random_forest":
          return RandomForestRegressor(**func_args)
      elif action == "neural_network":
          return MLPRegressor(**func_args)
      else:
          raise ValueError(
              "The function: {} is not supported by dowhy at the moment.".format(
                  action
              )
          )
  
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 6 for calling a global function with example usage: _get_regressor_object(action) and returns: LinearRegression, KNeighborsRegressor, SVR, RandomForestRegressor, MLPRegressor