class_docstr: Sample negative spot and asoc to construct SSI
class_docstr_tok: ["Sample","negative","spot","and","asoc","to","construct","SSI"]
code: def get_ordered_dict(schema_name_list, tokenizer):
      schema_ordered_dict = OrderedDict()
      for name in schema_name_list:
          schema_ordered_dict[name] = tokenizer.encode(
              name, add_special_tokens=False
          )
      return schema_ordered_dict
  
imports: import os
  from dataclasses import dataclass
  import torch
  import logging
  import random
  import math
  from typing import Optional, Union
  from collections import OrderedDict
  from transformers import PreTrainedTokenizerBase, PreTrainedModel
  from transformers.file_utils import PaddingStrategy
  from ...extraction.record_schema import RecordSchema
  from ...extraction.dataset_processer import spot_prompt, asoc_prompt
  from ...extraction.constants import BaseStructureMarker, text_start
  from ...extraction.utils import convert_to_record_function
  from ...extraction.noiser.spot_asoc_noiser import SpotAsocNoiser
label_desc: Use image node_id 2 for calling the DynamicSSIGenerator obj's underlying member method code with example usage: obj.get_ordered_dict(schema_name_list, tokenizer) and returns: schema_ordered_dict