code: def prepare_inputs_for_generation(
      self, input_ids, past_key_values=None, **kwargs
  ):
      if past_key_values is not None:
          past_length = past_key_values[0][0].shape[2]
          # Some generation methods already pass only the last input ID
          if input_ids.shape[1] > past_length:
              remove_prefix_length = past_length
          else:
              # Default to old behavior: keep only final ID
              remove_prefix_length = input_ids.shape[1] - 1
          input_ids = input_ids[:, remove_prefix_length:]
  
      attention_mask = kwargs.get("attention_mask", None)
      use_cache = kwargs.get("use_cache", None)
  
      return {
          "input_ids": input_ids,
          "past_key_values": past_key_values,
          "use_cache": use_cache,
          "position_ids": None,
          "attention_mask": attention_mask,
      }
  
imports: import logging
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
  import numpy
  import onnx
  import torch
  from onnx.tools import update_model_dims
  from transformers import AutoModelForCausalLM, GenerationConfig
  from transformers.file_utils import add_end_docstrings, add_start_docstrings_to_model_forward
  from transformers.modeling_outputs import CausalLMOutputWithPast
  import onnxruntime
  from ..exporters.onnx import MODEL_TYPES_REQUIRING_POSITION_IDS, main_export
  from ..onnx.utils import check_model_uses_external_data
  from ..utils import NormalizedConfigManager, check_if_transformers_greater
  from ..utils.modeling_utils import MODEL_TO_PATCH_FOR_PAST
  from ..utils.save_utils import maybe_save_preprocessors
  from .constants import DECODER_MERGED_ONNX_FILE_PATTERN, DECODER_ONNX_FILE_PATTERN, DECODER_WITH_PAST_ONNX_FILE_PATTERN
  from .modeling_ort import ONNX_MODEL_END_DOCSTRING, ORTModel
  from .models.bloom import bloom_convert_to_bloom_cache, bloom_convert_to_standard_cache
  from .utils import ONNX_DECODER_NAME, ONNX_DECODER_WITH_PAST_NAME, ONNX_WEIGHTS_NAME
label_desc: Use image node_id 1 for calling the ORTOPTForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values) and returns: dict