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(images=input_image, return_tensors=framework))

        return inputs

        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
        
dpkd/transformers/src/transformers/models/imagegpt/configuration_imagegpt.py