code: def prepare_inputs_for_generation(
      self, input_ids, past=None, attention_mask=None, **model_kwargs
  ):
      input_shape = input_ids.shape
      # 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:
          attention_mask = input_ids.new_ones(input_shape)
  
      # cut decoder_input_ids if past is used
      if past is not None:
          input_ids = input_ids[:, -1:]
  
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "past_key_values": past,
          "adapter_input_parallelized": model_kwargs.pop(
              "adapter_input_parallelized", False
          ),
      }
  
file_path: adapters/src/adapters/models/xlm_roberta/adapter_model.py
imports: from transformers.models.xlm_roberta.modeling_xlm_roberta import XLM_ROBERTA_INPUTS_DOCSTRING, XLM_ROBERTA_START_DOCSTRING, XLMRobertaModel, XLMRobertaPreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...context import AdapterSetup
  from ...heads import BertStyleMaskedLMHead, BiaffineParsingHead, CausalLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, MultipleChoiceHead, QuestionAnsweringHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 3 for calling the XLMRobertaAdapterModel obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past, attention_mask) and returns: dict