code: def prepare_inputs_for_generation(
      self,
      decoder_input_ids,
      past=None,
      attention_mask=None,
      head_mask=None,
      decoder_head_mask=None,
      cross_attn_head_mask=None,
      use_cache=None,
      encoder_outputs=None,
      **kwargs,
  ):
      # cut decoder_input_ids if past is used
      if past is not None:
          decoder_input_ids = decoder_input_ids[:, -1:]
  
      return {
          "input_ids": None,  # encoder_outputs is defined. input_ids not needed
          "encoder_outputs": encoder_outputs,
          "past_key_values": past,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
          "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
      }
  
imports: import math
  import random
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...deepspeed import is_deepspeed_zero3_enabled
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_m2m_100 import M2M100Config
label_desc: Use image node_id 8 for calling the M2M100ForConditionalGeneration obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(decoder_input_ids, past, attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, use_cache, encoder_outputs) and returns: dict