code: def generate_dummy_inputs(
      self,
      tokenizer: "PreTrainedTokenizerBase",
      batch_size: int = -1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional[TensorType] = None,
  ) -> Mapping[str, Any]:
      # TODO: should we set seq_length = 1 when self.use_past = True?
      common_inputs = super().generate_dummy_inputs(
          tokenizer,
          batch_size=batch_size,
          seq_length=seq_length,
          is_pair=is_pair,
          framework=framework,
      )
  
      if self.use_past:
          if not is_torch_available():
              raise ValueError(
                  "Cannot generate dummy past_keys inputs without PyTorch installed."
              )
          else:
              import torch
  
          batch, seqlen = common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          past_key_values_length = seqlen + 2
          shape = (
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
  
          if "attention_mask" in common_inputs:
              mask_dtype = common_inputs["attention_mask"].dtype
              common_inputs["attention_mask"] = torch.cat(
                  [
                      common_inputs["attention_mask"],
                      torch.ones(
                          batch,
                          past_key_values_length,
                          dtype=mask_dtype,
                      ),
                  ],
                  dim=1,
              )
  
          common_inputs["past_key_values"] = []
          for _ in range(self.num_layers):
              common_inputs["past_key_values"].append(
                  (torch.zeros(shape), torch.zeros(shape))
              )
  
      return common_inputs
  
imports: import copy
  import dataclasses
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union
  import numpy
  from packaging import version
  from ..utils import TensorType, is_torch_available, is_vision_available, logging
  from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size
label_desc: Use image node_id 7 for calling the OnnxConfigWithPast 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