docstr: Returns table of models available in the model library.
  
  Example
  -------
  >>> from pycaret.datasets import get_data
  >>> boston = get_data('boston')
  >>> from pycaret.regression import *
  >>> exp_name = setup(data = boston,  target = 'medv')
  >>> all_models = models()
  
  
  type: str, default = None
      - linear : filters and only return linear models
      - tree : filters and only return tree based models
      - ensemble : filters and only return ensemble 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
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code: def models(
      self,
      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
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> all_models = models()
  
  
      type: str, default = None
          - linear : filters and only return linear models
          - tree : filters and only return tree based models
          - ensemble : filters and only return ensemble 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 super().models(
          type=type, internal=internal, raise_errors=raise_errors
      )
  
imports: import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from pycaret.containers.metrics.regression import get_all_metric_containers
  from pycaret.containers.models.regression import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 21 for calling the RegressionExperiment obj's underlying member method code with example usage: obj.models(type, internal, raise_errors) and returns: super