code: def test_explain_xgboost_booster(boston_train):
      xs, ys, feature_names = boston_train
      booster = xgboost.train(
          params={"objective": "reg:linear", "silent": True},
          dtrain=xgboost.DMatrix(xs, label=ys),
      )
      res = explain_weights(booster)
      for expl in format_as_all(res, booster):
          assert "f12" in expl
      res = explain_weights(booster, feature_names=feature_names)
      for expl in format_as_all(res, booster):
          assert "LSTAT" in expl
  
imports: from __future__ import absolute_import
  import pytest
  import numpy
  import scipy.sparse
  from sklearn.feature_extraction.text import CountVectorizer
  import xgboost
  from xgboost import XGBClassifier, XGBRegressor
  from sklearn.pipeline import FeatureUnion
  from sklearn.preprocessing import FunctionTransformer
  from eli5.xgboost import _parse_tree_dump, _xgb_n_targets, _missing_values_set_to_nan, _parent_value, _parse_dump_line, _check_booster_args
  from eli5.explain import explain_prediction, explain_weights
  from eli5.formatters.text import format_as_text
  from eli5.formatters import fields
  from .utils import format_as_all, get_all_features, check_targets_scores
  from .test_sklearn_explain_weights import test_explain_tree_classifier, test_explain_random_forest_and_tree_feature_filter, test_feature_importances_no_remaining, assert_tree_classifier_explained
  from .test_sklearn_explain_prediction import assert_linear_regression_explained, assert_trained_linear_regression_explained, assert_explain_prediction_single_target, test_explain_clf_binary_iris, test_explain_prediction_pandas
label_desc: Use image node_id 6 for calling a global function with example usage: test_explain_xgboost_booster(boston_train) without return types