docstr: Generate inputs to provide to the ONNX exporter for the specific framework
  
  Args:
      processor ([`ProcessorMixin`]):
          The processor associated with this model configuration.
      batch_size (`int`, *optional*, defaults to -1):
          The batch size to export the model for (-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 processor 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, Any]: holding the kwargs to provide to the model's forward function
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code: def generate_dummy_inputs(
      self,
      processor: "ProcessorMixin",
      batch_size: int = -1,
      seq_length: 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]:
      """
      Generate inputs to provide to the ONNX exporter for the specific framework
  
      Args:
          processor ([`ProcessorMixin`]):
              The processor associated with this model configuration.
          batch_size (`int`, *optional*, defaults to -1):
              The batch size to export the model for (-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 processor 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, Any]: holding the kwargs to provide to the model's forward function
      """
  
      # A dummy image is used so OCR should not be applied
      setattr(processor.image_processor, "apply_ocr", False)
  
      # 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 = processor.tokenizer.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_text = [
          [" ".join([processor.tokenizer.unk_token]) * seq_length]
      ] * batch_size
  
      # Generate dummy bounding boxes
      dummy_bboxes = [[[48, 84, 73, 128]]] * batch_size
  
      # 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_image = self._generate_dummy_images(
          batch_size, num_channels, image_height, image_width
      )
  
      inputs = dict(
          processor(
              dummy_image,
              text=dummy_text,
              boxes=dummy_bboxes,
              return_tensors=framework,
          )
      )
  
      return inputs
  
file_path: transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Mapping, Optional
  from packaging import version
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import logging
label_desc: Use image node_id 4 for calling the LayoutLMv3OnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(processor, batch_size, seq_length, is_pair, framework, num_channels, image_width, image_height) and returns: inputs