code: def fill_with_past_key_values_(
      self,
      inputs_or_outputs: Mapping[str, Mapping[int, str]],
      direction: str,
  ):
      if direction not in ["inputs", "outputs"]:
          raise ValueError(
              f'direction must either be "inputs" or "outputs", but {direction} was given'
          )
  
      name = "past_key_values" if direction == "inputs" else "present"
  
      # 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"
      )
  
      encoder_sequence = "past_encoder_sequence"
      decoder_sequence = (
          "past_decoder_sequence"
          if direction == "inputs"
          else "past_decoder_sequence + sequence"
      )
  
      for i in range(min_num_layers):
          inputs_or_outputs[f"{name}.{i}.decoder.key"] = {
              0: "batch",
              2: decoder_sequence,
          }
          inputs_or_outputs[f"{name}.{i}.decoder.value"] = {
              0: "batch",
              2: decoder_sequence,
          }
          inputs_or_outputs[f"{name}.{i}.encoder.key"] = {
              0: "batch",
              2: encoder_sequence,
          }
          inputs_or_outputs[f"{name}.{i}.encoder.value"] = {
              0: "batch",
              2: encoder_sequence,
          }
  
      for i in range(min_num_layers, max_num_layers):
          if remaining_side_name == "encoder":
              axes_info = {0: "batch", 2: encoder_sequence}
          else:
              axes_info = {0: "batch", 2: decoder_sequence}
          inputs_or_outputs[
              f"{name}.{i}.{remaining_side_name}.key"
          ] = axes_info
  
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 5 for calling the OnnxSeq2SeqConfigWithPast obj's underlying member method code with example usage: obj.fill_with_past_key_values_(inputs_or_outputs, direction) without return types