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 __init__(
      self, path_context: str, random_state: int = 0, **kwargs
  ):
      (
          self.path,
          self.model_context,
          self.save_path,
      ) = self.create_contexts(path_context)
  
      self.path_context_og: str = path_context  # Saves path_context used to create the original context of the learner to enable sub-fits.
      self.is_trainer_present: bool = False
      self.trainer: Optional[AbstractTrainer] = None
      self.trainer_type: Optional[Type] = None
      self.trainer_path: Optional[str] = None
      self.reset_paths: bool = False
  
      if random_state is None:
          random_state = random.randint(0, 1000000)
      self.random_state = random_state
  
      try:
          from ..version import __version__  # noqa
  
          self.version = __version__
      except ImportError:
          self.version = None
      self._python_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
  
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 1 to create a new AbstractLearner object  with example: obj = AbstractLearner(path_context, random_state)