class_docstr: Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
code: def _generate_dummy_images(
      self,
      batch_size: int = 2,
      num_channels: int = 3,
      image_height: int = 40,
      image_width: int = 40,
  ):
      images = []
      for _ in range(batch_size):
          data = (
              np.random.rand(image_height, image_width, num_channels)
              * 255
          )
          images.append(
              Image.fromarray(data.astype("uint8")).convert("RGB")
          )
      return images
  
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 13 for calling the OnnxConfig obj's underlying member method code with example usage: obj._generate_dummy_images(batch_size, num_channels, image_height, image_width) and returns: images