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,
  ) -> Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
  
      if isinstance(preprocessor, PreTrainedTokenizerBase):
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          batch_size = compute_effective_axis_dimension(
              batch_size,
              fixed_dimension=OnnxConfig.default_fixed_batch,
              num_token_to_add=0,
          )
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          token_to_add = preprocessor.num_special_tokens_to_add(is_pair)
          seq_length = compute_effective_axis_dimension(
              seq_length,
              fixed_dimension=OnnxConfig.default_fixed_sequence,
              num_token_to_add=token_to_add,
          )
          # Generate dummy inputs according to compute batch and sequence
          dummy_input = [" ".join(["a"]) * seq_length] * batch_size
          inputs = dict(
              preprocessor(dummy_input, return_tensors=framework)
          )
          inputs["inputs"] = inputs.pop("input_ids")
          return inputs
      elif (
          isinstance(preprocessor, FeatureExtractionMixin)
          and preprocessor.model_input_names[0] == "pixel_values"
      ):
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          batch_size = compute_effective_axis_dimension(
              batch_size, fixed_dimension=OnnxConfig.default_fixed_batch
          )
          dummy_input = self._generate_dummy_images(
              batch_size, num_channels, image_height, image_width
          )
          inputs = dict(
              preprocessor(images=dummy_input, return_tensors=framework)
          )
          inputs["inputs"] = inputs.pop("pixel_values")
          return inputs
      else:
          raise ValueError(
              "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."
          )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/perceiver/configuration_perceiver.py
imports: from collections import OrderedDict
  from typing import Any, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...feature_extraction_utils import FeatureExtractionMixin
  from ...onnx import OnnxConfig
  from ...onnx.utils import compute_effective_axis_dimension
  from ...tokenization_utils_base import PreTrainedTokenizerBase
  from ...utils import TensorType, logging
label_desc: Use image node_id 3 for calling the PerceiverOnnxConfig 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) and returns: inputs, inputs