code: def convert_to_network(
      x: np.ndarray,
      net_size: Tuple[int, int],
      clip_min: float,
      clip_max: float,
  ) -> np.ndarray:
      """
      Convert image to network format.
  
      :param x: Input image.
      :param net_size: The resolution to resize to in (w, h).
      :param clip_min: Minimum value of an example.
      :param clip_max: Maximum value of an example.
      """
      import cv2
  
      if net_size is not None:
          x = cv2.resize(x, net_size)
      x = np.clip(x, 0, 1)
      x = x * (clip_max - clip_min) + clip_min
      if len(x.shape) < 3:
          x = x[:, :, np.newaxis]
      return x
  
imports: from typing import Optional, Tuple, Union, TYPE_CHECKING, List
  import math
  import numpy
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification import ClassifierMixin
  from art.utils import get_labels_np_array
label_desc: Use image node_id 2 for calling a global function with example usage: convert_to_network(x, net_size, clip_min, clip_max) and returns: x