code: def add_new_tokens_to_tokenizer(
      concept_str: str,
      initializer_strs: str,
      tokenizer: nn.Module,
  ) -> Tuple[torch.Tensor, List[int], str]:
      """Helper function for adding new tokens to the tokenizer and extending the corresponding
      embeddings appropriately, given a single concept token and its sequence of corresponding
      initializer tokens.  Returns the tensor of ids for the initializer tokens and their dummy
      replacements, as well as the string representation of the dummies.
      """
      assert not token_exists_in_tokenizer(
          concept_str, tokenizer
      ), f"concept_str {concept_str} already exists in tokenizer."
  
      initializer_ids = tokenizer(
          initializer_strs,
          return_tensors="pt",
          add_special_tokens=False,
      ).input_ids[0]
  
      # Add a dummy placeholder token for every token in the initializer.
      dummy_placeholder_str_list = [
          f"<{concept_str}>_{n}" for n in range(len(initializer_ids))
      ]
      # Sanity check.
      for dummy in dummy_placeholder_str_list:
          assert not token_exists_in_tokenizer(
              dummy, tokenizer
          ), f"dummy {dummy} already exists in tokenizer."
  
      dummy_placeholder_strs = " ".join(dummy_placeholder_str_list)
  
      tokenizer.add_tokens(dummy_placeholder_str_list)
      dummy_placeholder_ids = tokenizer.convert_tokens_to_ids(
          dummy_placeholder_str_list
      )
      # Sanity check that the dummies correspond to the correct number of ids.
      assert len(dummy_placeholder_ids) == len(
          initializer_ids
      ), 'Length of "dummy_placeholder_ids" and "initializer_ids" must match.'
  
      return (
          initializer_ids,
          dummy_placeholder_ids,
          dummy_placeholder_strs,
      )
  
imports: from typing import List, Tuple
  import torch
  import torch.nn
label_desc: Use image node_id 1 for calling a global function with example usage: add_new_tokens_to_tokenizer(concept_str, initializer_strs, tokenizer) and returns: initializer_ids,  dummy_placeholder_ids,  dummy_placeholder_strs