class_docstr: Document Classification with Huggingface backbones. Inherit from HFAutoModelForTextPrediction.
docstr: Check the tokenizer to see if it is a text only tokenizer.
  
  Parameters
  ----------
  tokenizer
      The tokenizer to be used.
  
  Returns
  -------
  True if the tokenizer only accept text, otherwise, False.
docstr_tok: ["Check","the","tokenizer","to","see","if","it","is","a","text","only","tokenizer",".","Parameters","--","--","--","--","--","tokenizer","The","tokenizer","to","be","used",".","Returns","--","--","--","-","True","if","the","tokenizer","only","accept","text",",","otherwise",",","False","."]
code: def is_text_only(self):
      """
      Check the tokenizer to see if it is a text only tokenizer.
  
      Parameters
      ----------
      tokenizer
          The tokenizer to be used.
  
      Returns
      -------
      True if the tokenizer only accept text, otherwise, False.
      """
      model_args = list(
          inspect.signature(self.tokenizer.__call__).parameters.keys()
      )
      # Tokenizers of document foundation models usually have a "boxes" argument.
      if "boxes" not in model_args:
          return True
      else:
          return False
  
file_path: autogluon/multimodal/src/autogluon/multimodal/models/document_transformer.py
imports: import inspect
  import logging
  from typing import Callable, Dict, List, Optional, Tuple, Union, cast
  from transformers import logging
  from ..constants import ATTENTION_MASK, AUTOMM, BBOX, COLUMN, COLUMN_FEATURES, FEATURES, IMAGE, INPUT_IDS, LABEL, LOGITS, MASKS, PIXEL_VALUES, TEXT_SEGMENT_IDS, TEXT_TOKEN_IDS, TEXT_VALID_LENGTH, TOKEN_TYPE_IDS
  from .huggingface_text import HFAutoModelForTextPrediction
  from .utils import get_column_features
label_desc: Use image node_id 2 for calling the DocumentTransformer obj's underlying member method code with example usage: obj.is_text_only() and returns: True, False