code: def get_name(estimator):
      name = estimator.__class__.__name__
      if name == "Pipeline":
          name = [get_name(est[1]) for est in estimator.steps]
          name = " + ".join(name)
      return name
  
file_path: scikit-learn/examples/preprocessing/plot_discretization_classification.py
imports: import matplotlib.pyplot
  import numpy
  from matplotlib.colors import ListedColormap
  from sklearn.datasets import make_circles, make_classification, make_moons
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import KBinsDiscretizer, StandardScaler
  from sklearn.svm import SVC, LinearSVC
  from sklearn.utils._testing import ignore_warnings
label_desc: Use image node_id 1 for calling a global function with example usage: get_name(estimator) and returns: name