code: def _get_net_size(self, idx):
      if idx % 10 == 0:
          net_size = self.downsample * np.random.randint(
              self.min_net_size / self.downsample,
              self.max_net_size / self.downsample + 1,
          )
  
          self.net_h, self.net_w = net_size, net_size
      return self.net_h, self.net_w
  
file_path: ImageAI/imageai_tf_deprecated/Detection/Custom/generator.py
imports: import cv2
  import copy
  import numpy
  from tensorflow.keras.utils import Sequence
  from imageai.Detection.Custom.utils.bbox import BoundBox, bbox_iou
  from imageai.Detection.Custom.utils.image import apply_random_scale_and_crop, random_distort_image, random_flip, correct_bounding_boxes
label_desc: Use image node_id 4 for calling the BatchGenerator obj's underlying member method code with example usage: obj._get_net_size(idx) and returns: self,  self