code: def prepare_seq2seq_batch(
      self,
      src_texts: List[str],
      tgt_texts: Optional[List[str]] = None,
      max_length: Optional[int] = None,
      max_target_length: Optional[int] = None,
      padding: str = "longest",
      return_tensors: str = None,
      truncation: bool = True,
      **kwargs,
  ) -> BatchEncoding:
      warnings.warn(
          "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
          "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
          "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
          "details",
          FutureWarning,
      )
      if max_length is None:
          max_length = self.current_tokenizer.model_max_length
      model_inputs = self(
          src_texts,
          add_special_tokens=True,
          return_tensors=return_tensors,
          max_length=max_length,
          padding=padding,
          truncation=truncation,
          **kwargs,
      )
      if tgt_texts is None:
          return model_inputs
      # Process tgt_texts
      if max_target_length is None:
          max_target_length = self.current_tokenizer.model_max_length
      labels = self(
          text_target=tgt_texts,
          add_special_tokens=True,
          return_tensors=return_tensors,
          padding=padding,
          max_length=max_target_length,
          truncation=truncation,
          **kwargs,
      )
      model_inputs["labels"] = labels["input_ids"]
      return model_inputs
  
file_path: transformers/src/transformers/models/rag/tokenization_rag.py
imports: import os
  import warnings
  from typing import List, Optional
  from ...tokenization_utils_base import BatchEncoding
  from ...utils import logging
  from .configuration_rag import RagConfig
label_desc: Use image node_id 9 for calling the RagTokenizer obj's underlying member method code with example usage: obj.prepare_seq2seq_batch(src_texts, tgt_texts, max_length, max_target_length, padding, return_tensors, truncation) and returns: model_inputs, model_inputs