code: def models(
      type: Optional[str] = None,
      internal: bool = False,
      raise_errors: bool = True,
  ) -> pd.DataFrame:
      """
      Returns table of models available in the model library.
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> data = get_data('airline')
      >>> from pycaret.time_series import *
      >>> exp_name = setup(data = data, fh = 12)
      >>> models()
  
  
      type: str, default = None
          - baseline : filters and only return baseline models
          - classical : filters and only return classical models
          - linear : filters and only return linear models
          - tree : filters and only return tree based models
          - neighbors : filters and only return neighbors models
  
  
      internal: bool, default = False
          When True, will return extra columns and rows used internally.
  
  
      raise_errors: bool, default = True
          When False, will suppress all exceptions, ignoring models
          that couldn't be created.
  
  
      Returns:
          pandas.DataFrame
  
      """
      return _CURRENT_EXPERIMENT.models(
          type=type, internal=internal, raise_errors=raise_errors
      )
  
imports: import logging
  import os
  from typing import TYPE_CHECKING, Any, BinaryIO, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.time_series.forecasting.oop import TSForecastingExperiment
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 15 for calling a global function with example usage: models(type, internal, raise_errors) and returns: _CURRENT_EXPERIMENT