class_docstr: Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
code: def _generate_dummy_audio(
      self,
      batch_size: int = 2,
      sampling_rate: int = 22050,
      time_duration: float = 5.0,
      frequency: int = 220,
  ):
      audio_data = []
      for _ in range(batch_size):
          # time variable
          t = np.linspace(
              0,
              time_duration,
              int(time_duration * sampling_rate),
              endpoint=False,
          )
  
          # generate pure sine wave at `frequency` Hz
          audio_data.append(0.5 * np.sin(2 * np.pi * frequency * t))
  
      return audio_data
  
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 14 for calling the OnnxConfig obj's underlying member method code with example usage: obj._generate_dummy_audio(batch_size, sampling_rate, time_duration, frequency) and returns: audio_data