code: def __init__(self, trainer_type=AutoTrainer, **kwargs):
      super().__init__(**kwargs)
      self.trainer_type = trainer_type
      self.class_weights = None
      self._time_fit_total = None
      self._time_fit_preprocessing = None
      self._time_fit_training = None
      self._time_limit = None
      self.preprocess_1_time = (
          None  # Time required to preprocess 1 row of data
      )
      self.preprocess_1_batch_size = None
  
imports: import copy
  import logging
  import math
  import time
  import numpy
  import pandas
  from pandas import DataFrame
  from autogluon.common.utils.log_utils import convert_time_in_s_to_log_friendly
  from autogluon.common.utils.system_info import get_ag_system_info
  from autogluon.core.constants import AUTO_WEIGHT, BALANCE_WEIGHT, BINARY, MULTICLASS, QUANTILE, REGRESSION
  from autogluon.core.data import LabelCleaner
  from autogluon.core.data.cleaner import Cleaner
  from autogluon.core.utils.time import sample_df_for_time_func, time_func
  from autogluon.core.utils.utils import augment_rare_classes, extract_column
  from ..trainer import AutoTrainer
  from .abstract_learner import AbstractTabularLearner
label_desc: Use image node_id 1 to create a new DefaultLearner object from inherited base classes: AbstractTabularLearner with example: obj = DefaultLearner(trainer_type)