code: def noise(outcome: np.ndarray, std_dev: float):
      """
      Add white noise with mean 0 and standard deviation = std_dev
  
      :param 'outcome': np.ndarray
          The outcome variable, to which the white noise is added.
      :param 'std_dev': float
          The standard deviation of the white noise.
  
      :returns: outcome with added noise
      """
      return outcome + np.random.normal(
          scale=std_dev, size=outcome.shape[0]
      )
  
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 8 for calling a global function with example usage: noise(outcome, std_dev) and returns: unknown