class_docstr: Abstract generator class.
      
docstr: Randomly transforms image and annotation.
          
code: def random_transform_group_entry(
      self, image, annotations, transform=None
  ):
      """Randomly transforms image and annotation."""
      # randomly transform both image and annotations
      if transform is not None or self.transform_generator:
          if transform is None:
              transform = adjust_transform_for_image(
                  next(self.transform_generator),
                  image,
                  self.transform_parameters.relative_translation,
              )
  
          # apply transformation to image
          image = apply_transform(
              transform, image, self.transform_parameters
          )
  
          # Transform the bounding boxes in the annotations.
          annotations["bboxes"] = annotations["bboxes"].copy()
          for index in range(annotations["bboxes"].shape[0]):
              annotations["bboxes"][index, :] = transform_aabb(
                  transform, annotations["bboxes"][index, :]
              )
  
      return image, annotations
  
imports: import numpy
  import random
  import warnings
  from tensorflow import keras
  from ..utils.anchors import anchor_targets_bbox, anchors_for_shape, guess_shapes
  from ..utils.config import parse_anchor_parameters, parse_pyramid_levels
  from ..utils.image import TransformParameters, adjust_transform_for_image, apply_transform, preprocess_image, resize_image
  from ..utils.transform import transform_aabb
label_desc: Use image node_id 18 for calling the Generator obj's underlying member method code with example usage: obj.random_transform_group_entry(image, annotations, transform) and returns: image,  annotations