code: def generate_dummy_inputs(
      self,
      preprocessor: Union[
          "PreTrainedTokenizerBase", "FeatureExtractionMixin"
      ],
      batch_size: int = -1,
      seq_length: int = -1,
      num_choices: int = -1,
      is_pair: bool = False,
      framework: Optional["TensorType"] = None,
      num_channels: int = 3,
      image_width: int = 40,
      image_height: int = 40,
      tokenizer: "PreTrainedTokenizerBase" = None,
  ) -> Mapping[str, Any]:
      dummy_inputs = super().generate_dummy_inputs(
          preprocessor=preprocessor, framework=framework
      )
      if (
          self._config.type_vocab_size == 0
          and "token_type_ids" in dummy_inputs
      ):
          del dummy_inputs["token_type_ids"]
      return dummy_inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/deberta_v2/configuration_deberta_v2.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 3 for calling the DebertaV2OnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(preprocessor, batch_size, seq_length, num_choices, is_pair, framework, num_channels, image_width, image_height, tokenizer) and returns: dummy_inputs