class_docstr: Prepare NER data for the model specified by "prefix".
docstr: Extract one sample's multimodal data.
  
  Parameters
  ----------
  all
      All the raw input data.
  is_training
      Whether to do processing in the training mode.
  
  Returns
  -------
  A dictionary containing one sample's features and/or labels.
docstr_tok: ["Extract","one","sample","'s","multimodal","data",".","Parameters","--","--","--","--","--","all","All","the","raw","input","data",".","is_training","Whether","to","do","processing","in","the","training","mode",".","Returns","--","--","--","-","A","dictionary","containing","one","sample","'s","features","and\/or","labels","."]
code: def __call__(
      self,
      all_features: Dict[str, Union[NDArray, list]],
      feature_modalities: Dict[str, Union[NDArray, list]],
      is_training: bool,
  ) -> Dict:
      """
      Extract one sample's multimodal data.
  
      Parameters
      ----------
      all
          All the raw input data.
      is_training
          Whether to do processing in the training mode.
  
      Returns
      -------
      A dictionary containing one sample's features and/or labels.
      """
      ret = {}
      if self.prefix == NER_TEXT:
          ret = self.process_ner(
              all_features, feature_modalities, is_training
          )
  
      return ret
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/process_ner.py
imports: import logging
  import os
  import warnings
  from typing import Any, Dict, List, Optional, Union
  import numpy
  from nptyping import NDArray
  from omegaconf import DictConfig
  from torch import nn
  from ..constants import AUTOMM, NER_ANNOTATION, NER_TEXT, TEXT, TEXT_NER
  from .collator import PadCollator, StackCollator
  from .utils import process_ner_annotations, tokenize_ner_text
label_desc: Use image node_id 4 for calling the NerProcessor obj's underlying member method code with example usage: obj.__call__(all_features, feature_modalities, is_training) and returns: ret