code: def clf_diabetes(diabetes):
      clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
      clf.fit(diabetes.data, diabetes.target)
      return clf
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from scipy.stats.mstats import mquantiles
  from sklearn.compose import make_column_transformer
  from sklearn.datasets import load_diabetes, load_iris, make_classification, make_regression
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
  from sklearn.inspection import PartialDependenceDisplay
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.utils._testing import _convert_container
label_desc: Use image node_id 2 for calling a global function with example usage: clf_diabetes(diabetes) and returns: clf