code: def dec_tree_pipeline(
      x_dataset_classifier, y_dataset_classifier
  ) -> Pipeline:
      model = make_pipeline(
          VarianceThreshold(threshold=0.5),
          PolynomialFeatures(degree=2, interaction_only=True),
          DecisionTreeClassifier(max_depth=3),
      )
      model.fit(x_dataset_classifier, y_dataset_classifier)
      return model
  
file_path: dtreeviz/testing/testlib/models/test_decision_trees_sk_pipeline.py
imports: import numpy
  import pytest
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.feature_selection import VarianceThreshold
  from sklearn.pipeline import make_pipeline, Pipeline
  from sklearn.preprocessing import PolynomialFeatures
  from dtreeviz.models.sklearn_decision_trees import ShadowSKDTree
  from dtreeviz.utils import _extract_final_feature_names, extract_params_from_pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: dec_tree_pipeline(x_dataset_classifier, y_dataset_classifier) and returns: model