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_trainer(self) -> AbstractTrainer:
      if self.trainer is not None:
          return self.trainer
      else:
          if self.trainer_path is None:
              raise AssertionError("Trainer does not exist.")
          # trainer_path is used to determine if there's a trained trainer
          # model_context contains the new trainer_path with updated context
          return self.trainer_type.load(
              path=self.model_context, reset_paths=self.reset_paths
          )
  
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 12 for calling the AbstractLearner obj's underlying member method code with example usage: obj.load_trainer() and returns: self, self