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(cls, path_context, reset_paths=True):
      load_path = os.path.join(path_context, cls.learner_file_name)
      obj = load_pkl.load(path=load_path)
      if reset_paths:
          obj.set_contexts(path_context)
          if obj.trainer_path is not None:
              obj.trainer_path = obj.model_context
          obj.reset_paths = reset_paths
          # TODO: Still have to change paths of models in trainer + trainer object path variables
          return obj
      else:
          obj.set_contexts(obj.path_context)
          return obj
  
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 10 for calling the AbstractLearner obj's underlying member method code with example usage: obj.load(cls, path_context, reset_paths) and returns: obj, obj