code: def _segment_prompt_into_text_token_conversions(prompt: str) -> List:
      """
      Given a string prompt, converts the prompt into a list of TextTokenConversions.
      """
      # Wherever, we notice the [TOKEN_OPEN_STRING, TOKEN_CLOSE_STRING], we split the prompt
      prompt_text_list: List = []
      regex_pattern = re.compile(
          f"({TOKEN_BBOX_OPEN_STRING}|{TOKEN_BBOX_CLOSE_STRING}|{TOKEN_POINT_OPEN_STRING}|{TOKEN_POINT_CLOSE_STRING})"
      )
      # Split by the regex pattern
      prompt_split = regex_pattern.split(prompt)
      for i, elem in enumerate(prompt_split):
          if len(elem) == 0 or elem in [
              TOKEN_BBOX_OPEN_STRING,
              TOKEN_BBOX_CLOSE_STRING,
              TOKEN_POINT_OPEN_STRING,
              TOKEN_POINT_CLOSE_STRING,
          ]:
              continue
          prompt_text_list.append(
              (
                  elem,
                  i > 1
                  and prompt_split[i - 1]
                  in [TOKEN_BBOX_OPEN_STRING, TOKEN_POINT_OPEN_STRING],
              )
          )
      return prompt_text_list
  
imports: import re
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import PaddingStrategy, TruncationStrategy
  from ...utils import TensorType, is_torch_available, logging, requires_backends
label_desc: Use image node_id 4 for calling a global function with example usage: _segment_prompt_into_text_token_conversions(prompt) and returns: prompt_text_list