class_docstr: AutoMM is designed to simplify the fine-tuning of foundation models
  for downstream applications with just three lines of code.
  AutoMM seamlessly integrates with popular model zoos such as
  `HuggingFace Transformers <https://github.com/huggingface/transformers>`_,
  `TIMM <https://github.com/huggingface/pytorch-image-models>`_,
  and `MMDetection <https://github.com/open-mmlab/mmdetection>`_,
  accommodating a diverse range of data modalities,
  including image, text, tabular, and document data, whether used individually or in combination.
  It offers support for an array of tasks, encompassing classification, regression,
  object detection, named entity recognition, semantic matching, and image segmentation.
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docstr: List supported models for each problem type.
  
  Parameters
  ----------
  pretrained : bool, default = True
      If True, only return the models with pretrained weights.
      If False, return all the models as long as there is model definition.
  
  Returns
  -------
  a list of model names
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code: def list_supported_models(self, pretrained=True):
      """
      List supported models for each problem type.
  
      Parameters
      ----------
      pretrained : bool, default = True
          If True, only return the models with pretrained weights.
          If False, return all the models as long as there is model definition.
  
      Returns
      -------
      a list of model names
      """
      return self._learner.list_supported_models(pretrained=pretrained)
  
imports: from __future__ import annotations
  import json
  import logging
  import os
  import warnings
  from typing import Dict, List, Optional, Union
  import pandas
  import transformers
  from autogluon.common.utils.log_utils import set_logger_verbosity, verbosity2loglevel
  from autogluon.core.metrics import Scorer
  from .constants import AUTOMM_TUTORIAL_MODE, FEW_SHOT_CLASSIFICATION, NER, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, SEMANTIC_SEGMENTATION
  from .learners import BaseLearner, FewShotSVMLearner, MultiModalMatcher, NERLearner, ObjectDetectionLearner, SemanticSegmentationLearner
  from .problem_types import PROBLEM_TYPES_REG
  from .utils import get_dir_ckpt_paths
label_desc: Use image node_id 33 for calling the MultiModalPredictor obj's underlying member method code with example usage: obj.list_supported_models(pretrained) and returns: self