code: def prepare_inputs_for_generation(
      self, input_ids, past=None, **kwargs
  ):
      token_type_ids = kwargs.get("token_type_ids", None)
      # only last token for inputs_ids if past is defined in kwargs
      if past:
          input_ids = input_ids[:, -1].unsqueeze(-1)
          if token_type_ids is not None:
              token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
  
      attention_mask = kwargs.get("attention_mask", None)
      position_ids = kwargs.get("position_ids", None)
  
      if attention_mask is not None and position_ids is None:
          # create position_ids on the fly for batch generation
          position_ids = attention_mask.long().cumsum(-1) - 1
          position_ids.masked_fill_(attention_mask == 0, 1)
          if past:
              position_ids = position_ids[:, -1].unsqueeze(-1)
      else:
          position_ids = None
      return {
          "input_ids": input_ids,
          "past_key_values": past,
          "use_cache": kwargs.get("use_cache"),
          "position_ids": position_ids,
          "attention_mask": attention_mask,
          "token_type_ids": token_type_ids,
          "adapter_input_parallelized": kwargs.pop(
              "adapter_input_parallelized", False
          ),
      }
  
file_path: adapters/src/adapters/models/gpt2/adapter_model.py
imports: import logging
  import torch
  from adapters.heads.base import QuestionAnsweringHead
  from transformers.models.gpt2.modeling_gpt2 import GPT2_START_DOCSTRING, GPT2Model, GPT2PreTrainedModel
  from transformers.utils import add_start_docstrings
  from ...composition import adjust_tensors_for_parallel
  from ...heads import CausalLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 3 for calling the GPT2AdapterModel obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past) and returns: dict