code: def prepare_inputs_for_generation(
      self,
      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:
          input_ids = input_ids[:, -1:]
  
      return {
          "decoder_input_ids": input_ids,
          "past_key_values": past,
          "encoder_outputs": encoder_outputs,
          "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,
          "adapter_input_parallelized": kwargs.pop(
              "adapter_input_parallelized", False
          ),
      }
  
imports: import logging
  import torch
  from transformers.models.t5.modeling_t5 import T5_INPUTS_DOCSTRING, T5_START_DOCSTRING, T5Model, T5PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...composition import adjust_tensors_for_parallel
  from ...heads import ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, QuestionAnsweringHead, Seq2SeqLMHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 5 for calling the T5AdapterModel obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past, attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, use_cache, encoder_outputs) and returns: dict