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: Name of one pd.DataFrame column that has the query data in semantic matching tasks.
docstr_tok: ["Name","of","one","pd.DataFrame","column","that","has","the","query","data","in","semantic","matching","tasks","."]
code: def query(self):
      """
      Name of one pd.DataFrame column that has the query data in semantic matching tasks.
      """
      return self._learner.query
  
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 4 for calling the MultiModalPredictor obj's underlying member method code with example usage: obj.query() and returns: self