class_docstr: Prepare text data for the model specified by "prefix". For multiple models requiring text data,
  we need to create a TextProcessor for each related model so that they will have independent input.
class_docstr_tok: ["Prepare","text","data","for","the","model","specified","by","``","prefix","''",".","For","multiple","models","requiring","text","data",",","we","need","to","create","a","TextProcessor","for","each","related","model","so","that","they","will","have","independent","input","."]
code: def text_segment_ids_key(self):
      return f"{self.prefix}_{TEXT_SEGMENT_IDS}"
  
imports: import ast
  import logging
  import os
  import warnings
  from copy import deepcopy
  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 CHOICES_IDS, COLUMN, TEXT, TEXT_SEGMENT_IDS, TEXT_TOKEN_IDS, TEXT_VALID_LENGTH
  from .collator import PadCollator, StackCollator
  from .template_engine import TemplateEngine
  from .trivial_augmenter import TrivialAugment
  from .utils import extract_value_from_config, get_text_token_max_len, normalize_txt, register_encoding_decoding_error_handlers
label_desc: Use image node_id 3 for calling the TextProcessor obj's underlying member method code with example usage: obj.text_segment_ids_key() and returns: self, str, TEXT_SEGMENT_IDS