code: def prepare_inputs_for_generation(
      self,
      input_ids,
      past_key_values=None,
      attention_mask=None,
      use_cache=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:]
  
      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] :]
      else:
          position_ids = None
          # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
          if attention_mask is None:
              attention_mask = input_ids.new_ones(input_ids.shape)
      # first step, decoder_cached_states are empty
      return {
          "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
          "attention_mask": attention_mask,
          "position_ids": position_ids,
          "past_key_values": past_key_values,
          "use_cache": use_cache,
      }
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_xglm import XGLMConfig
label_desc: Use image node_id 7 for calling the XGLMForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values, attention_mask, use_cache) and returns: dict