docstr: Generate inputs to provide to the ONNX exporter for the specific framework
  
  Args:
      preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
          The preprocessor associated with this model configuration.
      batch_size (`int`, *optional*, defaults to -1):
          The batch size to export the model for (-1 means dynamic axis).
      num_choices (`int`, *optional*, defaults to -1):
          The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
      seq_length (`int`, *optional*, defaults to -1):
          The sequence length to export the model for (-1 means dynamic axis).
      is_pair (`bool`, *optional*, defaults to `False`):
          Indicate if the input is a pair (sentence 1, sentence 2)
      framework (`TensorType`, *optional*, defaults to `None`):
          The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
      num_channels (`int`, *optional*, defaults to 3):
          The number of channels of the generated images.
      image_width (`int`, *optional*, defaults to 40):
          The width of the generated images.
      image_height (`int`, *optional*, defaults to 40):
          The height of the generated images.
  
  Returns:
      Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
code: def generate_dummy_inputs(
      self,
      preprocessor: "FeatureExtractionMixin",
      batch_size: int = 1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional["TensorType"] = None,
      num_channels: int = 3,
      image_width: int = 32,
      image_height: int = 32,
  ) -> Mapping[str, Any]:
      """
      Generate inputs to provide to the ONNX exporter for the specific framework
  
      Args:
          preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
              The preprocessor associated with this model configuration.
          batch_size (`int`, *optional*, defaults to -1):
              The batch size to export the model for (-1 means dynamic axis).
          num_choices (`int`, *optional*, defaults to -1):
              The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
          seq_length (`int`, *optional*, defaults to -1):
              The sequence length to export the model for (-1 means dynamic axis).
          is_pair (`bool`, *optional*, defaults to `False`):
              Indicate if the input is a pair (sentence 1, sentence 2)
          framework (`TensorType`, *optional*, defaults to `None`):
              The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
          num_channels (`int`, *optional*, defaults to 3):
              The number of channels of the generated images.
          image_width (`int`, *optional*, defaults to 40):
              The width of the generated images.
          image_height (`int`, *optional*, defaults to 40):
              The height of the generated images.
  
      Returns:
          Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
      """
  
      input_image = self._generate_dummy_images(
          batch_size, num_channels, image_height, image_width
      )
      inputs = dict(preprocessor(input_image, framework))
  
      return inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/imagegpt/configuration_imagegpt.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Mapping, Optional
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the ImageGPTOnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(preprocessor, batch_size, seq_length, is_pair, framework, num_channels, image_width, image_height) and returns: inputs