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]:
      encoder_inputs = super(
          OnnxConfigWithPast, self
      ).generate_dummy_inputs(
          tokenizer,
          batch_size=batch_size,
          seq_length=seq_length,
          is_pair=is_pair,
          framework=framework,
      )
  
      # Generate decoder inputs
      decoder_seq_length = seq_length if not self.use_past else 1
      decoder_inputs = super(
          OnnxConfigWithPast, self
      ).generate_dummy_inputs(
          tokenizer,
          batch_size=batch_size,
          seq_length=decoder_seq_length,
          is_pair=is_pair,
          framework=framework,
      )
      decoder_inputs = {
          f"decoder_{name}": tensor
          for name, tensor in decoder_inputs.items()
      }
      common_inputs = dict(**encoder_inputs, **decoder_inputs)
  
      if self.use_past:
          if not is_torch_available():
              raise ValueError(
                  "Cannot generate dummy past_keys inputs without PyTorch installed."
              )
          else:
              import torch
          batch = common_inputs["input_ids"].shape[0]
          encoder_seq_length = common_inputs["input_ids"].shape[1]
          decoder_seq_length = common_inputs["decoder_input_ids"].shape[
              1
          ]
          (
              num_encoder_attention_heads,
              num_decoder_attention_heads,
          ) = self.num_attention_heads
          encoder_shape = (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          decoder_shape = (
              batch,
              num_decoder_attention_heads,
              # Not using the same length for past_key_values
              decoder_seq_length + 3,
              self._config.hidden_size // num_decoder_attention_heads,
          )
  
          common_inputs["past_key_values"] = []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          num_encoder_layers, num_decoder_layers = self.num_layers
          min_num_layers = min(num_encoder_layers, num_decoder_layers)
          max_num_layers = (
              max(num_encoder_layers, num_decoder_layers)
              - min_num_layers
          )
          remaining_side_name = (
              "encoder"
              if num_encoder_layers > num_decoder_layers
              else "decoder"
          )
  
          for _ in range(min_num_layers):
              # For encoder-decoder models, past_key_values contains pre-computed values for both the encoder and the
              # decoder layers, hence a tuple of 4 tensors instead of 2
              common_inputs["past_key_values"].append(
                  (
                      torch.zeros(decoder_shape),
                      torch.zeros(decoder_shape),
                      torch.zeros(encoder_shape),
                      torch.zeros(encoder_shape),
                  )
              )
  
          # TODO: test this.
          shape = (
              encoder_shape
              if remaining_side_name == "encoder"
              else decoder_shape
          )
          for _ in range(min_num_layers, max_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 4 for calling the OnnxSeq2SeqConfigWithPast 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