code: def prepare_inputs_for_generation(
      self,
      decoder_input_ids,
      past=None,
      use_cache=None,
      encoder_outputs=None,
      attention_mask=None,
      **kwargs,
  ):
      # cut decoder_input_ids if past is used
      if past is not None:
          decoder_input_ids = decoder_input_ids[:, -1:]
  
      return {
          "encoder_outputs": encoder_outputs,
          "past_key_values": past,
          "decoder_input_ids": decoder_input_ids,
          "use_cache": use_cache,
          "decoder_attention_mask": None,
      }
  
imports: import math
  import random
  from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_whisper import WhisperConfig
label_desc: Use image node_id 9 for calling the WhisperForConditionalGeneration obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(decoder_input_ids, past, use_cache, encoder_outputs, attention_mask) and returns: dict