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 refit_full(self, model: str = "all") -> Dict[str, str]:
      return self.load_trainer().refit_full(model=model)
  
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 11 for calling the TimeSeriesLearner obj's underlying member method code with example usage: obj.refit_full(model) and returns: self