docstr: Generate inputs to provide to the ONNX exporter for the specific framework
  
  Args:
      tokenizer: The tokenizer associated with this model configuration
      batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
      seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
      is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
      framework: The framework (optional) the tokenizer will generate tensor for
  
  Returns:
      Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
docstr_tok: ["Generate","inputs","to","provide","to","the","ONNX","exporter","for","the","specific","framework","Args",":","tokenizer",":","The","tokenizer","associated","with","this","model","configuration","batch_size",":","The","batch","size","(","int",")","to","export","the","model","for","(","-1","means","dynamic","axis",")","seq_length",":","The","sequence","length","(","int",")","to","export","the","model","for","(","-1","means","dynamic","axis",")","is_pair",":","Indicate","if","the","input","is","a","pair","(","sentence","1",",","sentence","2",")","framework",":","The","framework","(","optional",")","the","tokenizer","will","generate","tensor","for","Returns",":","Mapping","[","str",",","Tensor","]","holding","the","kwargs","to","provide","to","the","model","'s","forward","function"]
code: def generate_dummy_inputs(
      self,
      tokenizer: PreTrainedTokenizer,
      batch_size: int = -1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional[TensorType] = None,
  ) -> Mapping[str, Any]:
      """
      Generate inputs to provide to the ONNX exporter for the specific framework
  
      Args:
          tokenizer: The tokenizer associated with this model configuration
          batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
          seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
          is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
          framework: The framework (optional) the tokenizer will generate tensor for
  
      Returns:
          Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
      """
  
      input_dict = super().generate_dummy_inputs(
          tokenizer,
          batch_size=batch_size,
          seq_length=seq_length,
          is_pair=is_pair,
          framework=framework,
      )
  
      # Generate a dummy bbox
      box = [48, 84, 73, 128]
  
      if not framework == TensorType.PYTORCH:
          raise NotImplementedError(
              "Exporting LayoutLM to ONNX is currently only supported for PyTorch."
          )
  
      if not is_torch_available():
          raise ValueError(
              "Cannot generate dummy inputs without PyTorch installed."
          )
      import torch
  
      batch_size, seq_length = input_dict["input_ids"].shape
      input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(
          batch_size, 1, 1
      )
      return input_dict
  
file_path: transformers/src/transformers/models/layoutlm/configuration_layoutlm.py
imports: from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  from ...None import PretrainedConfig, PreTrainedTokenizer
  from ...onnx import OnnxConfig, PatchingSpec
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 3 for calling the LayoutLMOnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework) and returns: input_dict