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.
docstr: Create and return paths to save model objects, the learner object.
  
  Parameters
  ----------
  path_context: str
      Top-level directory where models and trainer will be saved.
docstr_tok: ["Create","and","return","paths","to","save","model","objects",",","the","learner","object",".","Parameters","--","--","--","--","--","path_context",":","str","Top-level","directory","where","models","and","trainer","will","be","saved","."]
code: def create_contexts(self, path_context: str):
      """Create and return paths to save model objects, the learner object.
  
      Parameters
      ----------
      path_context: str
          Top-level directory where models and trainer will be saved.
      """
      model_context = os.path.join(path_context, "models")
      save_path = os.path.join(path_context, self.learner_file_name)
      return path_context, model_context, save_path
  
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 2 for calling the AbstractLearner obj's underlying member method code with example usage: obj.create_contexts(path_context) and returns: path_context,  model_context,  save_path