code: def test_dp_ebm_external_privacy_bounds():
      data = synthetic_regression()
      X = data["full"]["X"]
      y = data["full"]["y"]
  
      # synthetic regression is all sampled from N(0, 1)
      privacy_bounds = {0: (-3, 3), 1: (-3, 3), 2: (-3, 3), 3: (-3, 3)}
  
      with warnings.catch_warnings():
          warnings.filterwarnings(
              "ignore", "Possible privacy violation*"
          )
  
          clf = DPExplainableBoostingRegressor(
              privacy_bounds=privacy_bounds,
              privacy_target_min=-3,
              privacy_target_max=3,
          )
          clf.fit(X, y)
          clf.predict(X)
  
          valid_ebm(clf)
  
imports: import math
  from sklearn.metrics import log_loss
  from interpret.utils import make_synthetic
  from ...tutils import synthetic_multiclass, synthetic_classification, iris_classification, smoke_test_explanations
  from ...tutils import synthetic_regression
  from interpret.glassbox import ExplainableBoostingRegressor, ExplainableBoostingClassifier
  from interpret.privacy import DPExplainableBoostingClassifier, DPExplainableBoostingRegressor
  from interpret.utils import inv_link
  from io import StringIO
  import json
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from sklearn.utils.estimator_checks import check_estimator
  import pytest
  import warnings
label_desc: Use image node_id 34 for calling a global function with example usage: test_dp_ebm_external_privacy_bounds() without return types