code: def generate_dummy_inputs(
      self,
      tokenizer: "PreTrainedTokenizerBase",
      batch_size: int = -1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional[TensorType] = None,
  ) -> Dict[str, Any]:
      import torch
  
      common_inputs = {}
      dummy_input = super().generate_dummy_inputs(
          tokenizer,
          batch_size=batch_size,
          seq_length=seq_length,
          is_pair=is_pair,
          framework=framework,
      )
      batch, encoder_seq_length = dummy_input["input_ids"].shape
      encoder_hidden_states_shape = (
          batch,
          encoder_seq_length,
          self._config.hidden_size,
      )
      common_inputs["input_ids"] = dummy_input.pop("decoder_input_ids")
      common_inputs["encoder_hidden_states"] = torch.zeros(
          encoder_hidden_states_shape
      )
      common_inputs["encoder_attention_mask"] = dummy_input.pop(
          "attention_mask"
      )
  
      if "past_key_values" in dummy_input:
          common_inputs["past_key_values"] = dummy_input.pop(
              "past_key_values"
          )
  
      return common_inputs
  
file_path: optimum/optimum/onnx/configuration.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Dict, Optional
  from transformers.file_utils import TensorType
  from transformers.utils import logging
  from transformers.onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
label_desc: Use image node_id 2 for calling the DecoderOnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework) and returns: common_inputs