code: def inputs(self) -> Mapping[str, Mapping[int, str]]:
      common_inputs = OrderedDict(
          [
              ("input_ids", {0: "batch", 1: "encoder_sequence"}),
              ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
          ]
      )
  
      if self.use_past:
          common_inputs["decoder_input_ids"] = {0: "batch"}
          common_inputs["decoder_attention_mask"] = {
              0: "batch",
              1: "past_decoder_sequence + sequence",
          }
      else:
          common_inputs["decoder_input_ids"] = {
              0: "batch",
              1: "decoder_sequence",
          }
          common_inputs["decoder_attention_mask"] = {
              0: "batch",
              1: "decoder_sequence",
          }
  
      if self.use_past:
          self.fill_with_past_key_values_(
              common_inputs, direction="inputs"
          )
      return common_inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py
imports: from collections import OrderedDict
  from typing import Any, Mapping, Optional
  from ...None import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 1 for calling the M2M100OnnxConfig obj's underlying member method code with example usage: obj.inputs() and returns: common_inputs