code: def insert_deep_learning_model(pipeline_step, file_name):
      # This is where we saved the random_name for this model
      random_name = pipeline_step.model
      # Load the Keras model here
      keras_file_name = (
          file_name[:-5] + random_name + "_keras_deep_learning_model.h5"
      )
  
      model = keras_load_model(keras_file_name)
  
      # Put the model back in place so that we can still use it to get predictions without having to load it back in from disk
      return model
  
file_path: auto_ml/auto_ml/utils_models.py
imports: import dill
  import os
  import random
  import sys
  from auto_ml import utils
  from auto_ml import utils_categorical_ensembling
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, ExtraTreesRegressor, AdaBoostRegressor, GradientBoostingRegressor, GradientBoostingClassifier, ExtraTreesClassifier, AdaBoostClassifier
  from sklearn.linear_model import RANSACRegressor, LinearRegression, Ridge, Lasso, ElasticNet, LassoLars, OrthogonalMatchingPursuit, BayesianRidge, ARDRegression, SGDRegressor, PassiveAggressiveRegressor, LogisticRegression, RidgeClassifier, SGDClassifier, Perceptron, PassiveAggressiveClassifier
  from sklearn.svm import LinearSVC, LinearSVR
  from sklearn.cluster import MiniBatchKMeans
label_desc: Use image node_id 4 for calling a global function with example usage: insert_deep_learning_model(pipeline_step, file_name) and returns: model