code: def _tokenize_prompts_with_image_and_batch(
      tokenizer,
      prompts: List[List[str]],
      scale_factors: Optional[List[List["torch.Tensor"]]],
      max_tokens_to_generate: int,
      max_position_embeddings: int,
      add_BOS: bool,  # Same issue with types as above
      add_beginning_of_answer_token: bool,
  ) -> Tuple["torch.Tensor", "torch.Tensor"]:
      """
      Given a set of prompts and number of tokens to generate:
      - tokenize prompts
      - set the sequence length to be the max of length of prompts plus the number of tokens we would like to generate
      - pad all the sequences to this length so we can convert them into a 3D tensor.
      """
  
      # If not tool use, tranform the coordinates while tokenizing
      if scale_factors is not None:
          transformed_prompt_tokens = []
          for prompt_seq, scale_factor_seq in zip(
              prompts, scale_factors
          ):
              transformed_prompt_tokens.append(
                  [
                      _transform_coordinates_and_tokenize(
                          prompt, scale_factor.item(), tokenizer
                      )
                      for prompt, scale_factor in zip(
                          prompt_seq, scale_factor_seq
                      )
                  ]
              )
      else:
          transformed_prompt_tokens = [
              [tokenizer.tokenize(prompt) for prompt in prompt_seq]
              for prompt_seq in prompts
          ]
  
      prompts_tokens = transformed_prompt_tokens
  
      if add_BOS:
          bos_token = tokenizer.vocab["<s>"]
      else:
          bos_token = tokenizer.vocab["|ENDOFTEXT|"]
      prompts_tokens = [
          [[bos_token] + x for x in prompt_seq]
          for prompt_seq in prompts_tokens
      ]
      if add_beginning_of_answer_token:
          boa = tokenizer.vocab[BEGINNING_OF_ANSWER_STRING]
          # Only add bbox open token to the last subsequence since that is what will be completed
          for token_seq in prompts_tokens:
              token_seq[-1].append(boa)
  
      # Now we have a list of list of tokens which each list has a different
      # size. We want to extend this list to:
      #   - incorporate the tokens that need to be generated
      #   - make all the sequences equal length.
      # Get the prompts length.
  
      prompts_length = [
          [len(x) for x in prompts_tokens_seq]
          for prompts_tokens_seq in prompts_tokens
      ]
      # Get the max prompts length.
      max_prompt_len: int = np.max(prompts_length)
      # Number of tokens in the each sample of the batch.
      samples_length = min(
          max_prompt_len + max_tokens_to_generate,
          max_position_embeddings,
      )
      if (
          max_prompt_len + max_tokens_to_generate
          > max_position_embeddings
      ):
          logger.warning(
              f"Max subsequence prompt length of {max_prompt_len} + max tokens to generate {max_tokens_to_generate}",
              f"exceeds context length of {max_position_embeddings}. Will generate as many tokens as possible.",
          )
      # Now update the list of list to be of the same size: samples_length.
      for prompt_tokens_seq, prompts_length_seq in zip(
          prompts_tokens, prompts_length
      ):
          for prompt_tokens, prompt_length in zip(
              prompt_tokens_seq, prompts_length_seq
          ):
              if len(prompt_tokens) > samples_length:
                  raise ValueError(
                      "Length of subsequence prompt exceeds sequence length."
                  )
              padding_size = samples_length - prompt_length
              prompt_tokens.extend(
                  [tokenizer.vocab["|ENDOFTEXT|"]] * padding_size
              )
  
      # Now we are in a structured format, we can convert to tensors.
      prompts_tokens_tensor = torch.tensor(
          prompts_tokens, dtype=torch.int64
      )
      prompts_length_tensor = torch.tensor(
          prompts_length, dtype=torch.int64
      )
  
      return prompts_tokens_tensor, prompts_length_tensor
  
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 7 for calling a global function with example usage: _tokenize_prompts_with_image_and_batch(tokenizer, prompts, scale_factors, max_tokens_to_generate, max_position_embeddings, add_BOS, add_beginning_of_answer_token) and returns: prompts_tokens_tensor,  prompts_length_tensor