code: def load_ml_model(file_name):
      with open(file_name, "rb") as read_file:
          base_pipeline = dill.load(read_file)
  
      if isinstance(
          base_pipeline,
          utils_categorical_ensembling.CategoricalEnsembler,
      ):
          for step in base_pipeline.transformation_pipeline.named_steps:
              pipeline_step = (
                  base_pipeline.transformation_pipeline.named_steps[
                      step
                  ]
              )
  
              try:
                  if (
                      pipeline_step.get(
                          "model_name", "reallylongnonsensicalstring"
                      )[:12]
                      == "DeepLearning"
                  ):
                      pipeline_step.model = insert_deep_learning_model(
                          pipeline_step, file_name
                      )
              except AttributeError:
                  pass
  
          for step in base_pipeline.trained_models:
              pipeline_step = base_pipeline.trained_models[step]
  
              try:
                  if (
                      pipeline_step.get(
                          "model_name", "reallylongnonsensicalstring"
                      )[:12]
                      == "DeepLearning"
                  ):
                      pipeline_step.model = insert_deep_learning_model(
                          pipeline_step, file_name
                      )
              except AttributeError:
                  pass
  
      else:
          for step in base_pipeline.named_steps:
              pipeline_step = base_pipeline.named_steps[step]
              try:
                  if (
                      pipeline_step.get(
                          "model_name", "reallylongnonsensicalstring"
                      )[:12]
                      == "DeepLearning"
                  ):
                      pipeline_step.model = insert_deep_learning_model(
                          pipeline_step, file_name
                      )
              except AttributeError:
                  pass
  
      return base_pipeline
  
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 5 for calling a global function with example usage: load_ml_model(file_name) and returns: base_pipeline