docstr: Fit a set of timeseries models specified by the `hyperparameters`
  dictionary that maps model names to their specified hyperparameters.
  
  Parameters
  ----------
  train_data: TimeSeriesDataFrame
      Training data for fitting time series timeseries models.
  hyperparameters: str or Dict
      A dictionary mapping selected model names, model classes or model factory to hyperparameter
      settings. Model names should be present in `trainer.presets.DEFAULT_MODEL_NAMES`. Optionally,
      the user may provide one of "default", "light" and "very_light" to specify presets.
  val_data: TimeSeriesDataFrame
      Optional validation data set to report validation scores on.
  hyperparameter_tune_kwargs
      Args for hyperparameter tuning
  excluded_model_types
      Names of models that should not be trained, even if listed in `hyperparameters`.
  time_limit
      Time limit for training
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code: def fit(
      self,
      train_data: TimeSeriesDataFrame,
      hyperparameters: Union[str, Dict[Any, Dict]],
      val_data: Optional[TimeSeriesDataFrame] = None,
      hyperparameter_tune_kwargs: Optional[Union[str, Dict]] = None,
      excluded_model_types: Optional[List[str]] = None,
      time_limit: Optional[float] = None,
  ):
      """
      Fit a set of timeseries models specified by the `hyperparameters`
      dictionary that maps model names to their specified hyperparameters.
  
      Parameters
      ----------
      train_data: TimeSeriesDataFrame
          Training data for fitting time series timeseries models.
      hyperparameters: str or Dict
          A dictionary mapping selected model names, model classes or model factory to hyperparameter
          settings. Model names should be present in `trainer.presets.DEFAULT_MODEL_NAMES`. Optionally,
          the user may provide one of "default", "light" and "very_light" to specify presets.
      val_data: TimeSeriesDataFrame
          Optional validation data set to report validation scores on.
      hyperparameter_tune_kwargs
          Args for hyperparameter tuning
      excluded_model_types
          Names of models that should not be trained, even if listed in `hyperparameters`.
      time_limit
          Time limit for training
      """
      self._train_multi(
          train_data,
          val_data=val_data,
          hyperparameters=hyperparameters,
          hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
          excluded_model_types=excluded_model_types,
          time_limit=time_limit,
      )
  
file_path: autogluon/timeseries/src/autogluon/timeseries/trainer/auto_trainer.py
imports: import logging
  from typing import Any, Dict, List, Optional, Union
  from ..models.presets import get_preset_models
  from .abstract_trainer import AbstractTimeSeriesTrainer, TimeSeriesDataFrame
label_desc: Use image node_id 2 for calling the AutoTimeSeriesTrainer obj's underlying member method code with example usage: obj.fit(train_data, hyperparameters, val_data, hyperparameter_tune_kwargs, excluded_model_types, time_limit) without return types