class_docstr: Abstract class for autogluon Learners. Learners encompass the learning problem end to end,
  including loading initial data, feature generation, model training, model prediction. Similar
  to Trainers, they implement `fit` and `predict` methods. The abstract method also includes
  concrete implementations of serialization/deserialization methods. Refer to individual
  documentations of concrete learner classes for further information.
code: def load_info(
      cls, path, reset_paths=True, load_model_if_required=True
  ):
      load_path = os.path.join(path, cls.learner_info_name)
      try:
          return load_pkl.load(path=load_path)
      except Exception as e:
          if load_model_if_required:
              learner = cls.load(
                  path_context=path, reset_paths=reset_paths
              )
              return learner.get_info()
          else:
              raise e
  
imports: import os
  import random
  import sys
  from typing import Optional, Type
  from autogluon.core.trainer import AbstractTrainer
  from autogluon.core.utils.loaders import load_pkl
  from autogluon.core.utils.savers import save_json, save_pkl
label_desc: Use image node_id 13 for calling the AbstractLearner obj's underlying member method code with example usage: obj.load_info(cls, path, reset_paths, load_model_if_required) and returns: load_pkl, learner