code: def prepare_inputs_for_generation(
      self,
      decoder_input_ids,
      attention_mask=None,
      use_cache=None,
      encoder_outputs=None,
      **kwargs,
  ):
      # cut decoder_input_ids if past is used
      if encoder_outputs 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,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
      }
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 4 for calling the RNNSeq2Seq obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(decoder_input_ids, attention_mask, use_cache, encoder_outputs) and returns: dict