class_docstr: TimeSeriesLearner encompasses a full time series learning problem for a
  training run, and keeps track of datasets, features, and the trainer object.
class_docstr_tok: ["TimeSeriesLearner","encompasses","a","full","time","series","learning","problem","for","a","training","run",",","and","keeps","track","of","datasets",",","features",",","and","the","trainer","object","."]
code: def __init__(
      self,
      path_context: str,
      target: str = "target",
      known_covariates_names: Optional[List[str]] = None,
      trainer_type: Type[
          AbstractTimeSeriesTrainer
      ] = AutoTimeSeriesTrainer,
      eval_metric: Union[str, TimeSeriesScorer, None] = None,
      eval_metric_seasonal_period: Optional[int] = None,
      prediction_length: int = 1,
      cache_predictions: bool = True,
      **kwargs,
  ):
      super().__init__(path_context=path_context)
      self.eval_metric: TimeSeriesScorer = check_get_evaluation_metric(
          eval_metric
      )
      self.eval_metric_seasonal_period = eval_metric_seasonal_period
      self.trainer_type = trainer_type
      self.target = target
      self.known_covariates_names = (
          []
          if known_covariates_names is None
          else known_covariates_names
      )
      self.prediction_length = prediction_length
      self.quantile_levels = kwargs.get(
          "quantile_levels",
          [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
      )
      self.cache_predictions = cache_predictions
  
      self.feature_generator = TimeSeriesFeatureGenerator(
          target=self.target,
          known_covariates_names=self.known_covariates_names,
      )
  
imports: import logging
  import reprlib
  import time
  from typing import Any, Dict, List, Optional, Type, Union
  import pandas
  from autogluon.core.learner import AbstractLearner
  from autogluon.timeseries.dataset.ts_dataframe import TimeSeriesDataFrame
  from autogluon.timeseries.metrics import TimeSeriesScorer, check_get_evaluation_metric
  from autogluon.timeseries.models.abstract import AbstractTimeSeriesModel
  from autogluon.timeseries.splitter import AbstractWindowSplitter
  from autogluon.timeseries.trainer import AbstractTimeSeriesTrainer, AutoTimeSeriesTrainer
  from autogluon.timeseries.utils.features import TimeSeriesFeatureGenerator
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
label_desc: Use image node_id 1 to create a new TimeSeriesLearner object from inherited base classes: AbstractLearner with example: obj = TimeSeriesLearner(path_context, target, known_covariates_names, trainer_type, eval_metric, eval_metric_seasonal_period, prediction_length, cache_predictions)