code: def bbox_transform_inv(boxes, deltas, mean=None, std=None):
      """Applies deltas (usually regression results) to boxes (usually anchors).
  
      Before applying the deltas to the boxes, the normalization that was previously applied (in the generator) has to be removed.
      The mean and std are the mean and std as applied in the generator. They are unnormalized in this function and then applied to the boxes.
  
      Args
          boxes : np.array of shape (B, N, 4), where B is the batch size, N the number of boxes and 4 values for (x1, y1, x2, y2).
          deltas: np.array of same shape as boxes. These deltas (d_x1, d_y1, d_x2, d_y2) are a factor of the width/height.
          mean  : The mean value used when computing deltas (defaults to [0, 0, 0, 0]).
          std   : The standard deviation used when computing deltas (defaults to [0.2, 0.2, 0.2, 0.2]).
  
      Returns
          A np.array of the same shape as boxes, but with deltas applied to each box.
          The mean and std are used during training to normalize the regression values (networks love normalization).
      """
      if mean is None:
          mean = [0, 0, 0, 0]
      if std is None:
          std = [0.2, 0.2, 0.2, 0.2]
  
      width = boxes[:, :, 2] - boxes[:, :, 0]
      height = boxes[:, :, 3] - boxes[:, :, 1]
  
      x1 = boxes[:, :, 0] + (deltas[:, :, 0] * std[0] + mean[0]) * width
      y1 = (
          boxes[:, :, 1] + (deltas[:, :, 1] * std[1] + mean[1]) * height
      )
      x2 = boxes[:, :, 2] + (deltas[:, :, 2] * std[2] + mean[2]) * width
      y2 = (
          boxes[:, :, 3] + (deltas[:, :, 3] * std[3] + mean[3]) * height
      )
  
      pred_boxes = keras.backend.stack([x1, y1, x2, y2], axis=2)
  
      return pred_boxes
  
imports: import tensorflow
  from tensorflow import keras
label_desc: Use image node_id 1 for calling a global function with example usage: bbox_transform_inv(boxes, deltas, mean, std) and returns: pred_boxes