code: def prepare_inputs_for_generation(
      self,
      input_ids,
      past_key_values=None,
      attention_mask=None,
      inputs_embeds=None,
      **kwargs,
  ):
      if past_key_values is not None:
          if isinstance(past_key_values, Cache):
              cache_length = past_key_values.get_seq_length()
              past_length = past_key_values.seen_tokens
              max_cache_length = past_key_values.get_max_length()
          else:
              cache_length = past_length = past_key_values[0][0].shape[
                  2
              ]
              max_cache_length = None
  
          # Keep only the unprocessed tokens:
          # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
          # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
          # input)
          if (
              attention_mask is not None
              and attention_mask.shape[1] > input_ids.shape[1]
          ):
              input_ids = input_ids[
                  :, -(attention_mask.shape[1] - past_length) :
              ]
          # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
          # input_ids based on the past_length.
          elif past_length < input_ids.shape[1]:
              input_ids = input_ids[:, past_length:]
          # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
  
          # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
          if (
              max_cache_length is not None
              and attention_mask is not None
              and cache_length + input_ids.shape[1] > max_cache_length
          ):
              attention_mask = attention_mask[:, -max_cache_length:]
  
      position_ids = kwargs.get("position_ids", None)
      if attention_mask is not None and position_ids is None:
          # create position_ids on the fly for batch generation
          position_ids = attention_mask.long().cumsum(-1) - 1
          position_ids.masked_fill_(attention_mask == 0, 1)
          if past_key_values:
              position_ids = position_ids[:, -input_ids.shape[1] :]
  
      # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
      if inputs_embeds is not None and past_key_values is None:
          model_inputs = {"inputs_embeds": inputs_embeds}
      else:
          model_inputs = {"input_ids": input_ids}
  
      model_inputs.update(
          {
              "position_ids": position_ids,
              "past_key_values": past_key_values,
              "use_cache": kwargs.get("use_cache"),
              "attention_mask": attention_mask,
          }
      )
      return model_inputs
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...cache_utils import Cache, DynamicCache
  from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_persimmon import PersimmonConfig
label_desc: Use image node_id 9 for calling the PersimmonForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values, attention_mask, inputs_embeds) and returns: model_inputs