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 predict(
      self,
      data: TimeSeriesDataFrame,
      known_covariates: Optional[TimeSeriesDataFrame] = None,
      model: Optional[Union[str, AbstractTimeSeriesModel]] = None,
      use_cache: bool = True,
      **kwargs,
  ) -> TimeSeriesDataFrame:
      data = self.feature_generator.transform(data)
      known_covariates = (
          self.feature_generator.transform_future_known_covariates(
              known_covariates
          )
      )
      known_covariates = self._align_covariates_with_forecast_index(
          known_covariates=known_covariates, data=data
      )
      return self.load_trainer().predict(
          data=data,
          known_covariates=known_covariates,
          model=model,
          use_cache=use_cache,
          **kwargs,
      )
  
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 6 for calling the TimeSeriesLearner obj's underlying member method code with example usage: obj.predict(data, known_covariates, model, use_cache) and returns: self