code: def tf_rf_model(dataset_spark_tensorflow):
      """
      The saved model is not the same with the raw one. When we load it, it is an instance of a _RandomForestInspector class
      Because of this, it would be better to recreate the same model and to be used in these unit tests.
      """
  
      random_state = 1234
      target_clf = "Survived"
      train_clf = tf_df.keras.pd_dataframe_to_tf_dataset(
          dataset_spark_tensorflow, label=target_clf
      )
      model_clf = tf_df.keras.RandomForestModel(
          max_depth=3, random_seed=random_state
      )
      model_clf.fit(train_clf)
  
      return model_clf
  
imports: import pytest
  import numpy
  import tensorflow_decision_forests
  from dtreeviz.models.tensorflow_decision_tree import ShadowTensorflowTree
label_desc: Use image node_id 2 for calling a global function with example usage: tf_rf_model(dataset_spark_tensorflow) and returns: model_clf