code: def prepare_inputs_for_generation(
      self,
      input_ids=None,
      query_embeds=None,
      past=None,
      attention_mask=None,
      use_cache=None,
      **kwargs,
  ):
      # 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:
          if input_ids is not None:
              attention_mask = input_ids.new_ones(input_ids.shape)
      if past:
          input_ids = input_ids[:, -1:]
          query_embeds = None
      # first step, decoder_cached_states are empty
      return {
          "input_ids": input_ids,
          "query_embeds": query_embeds,
          "attention_mask": attention_mask,
          "past_key_values": past,
          "use_cache": use_cache,
      }
  
imports: import random
  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 transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.opt.configuration_opt import OPTConfig
label_desc: Use image node_id 9 for calling the OPTForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, query_embeds, past, attention_mask, use_cache) and returns: dict