          ----------
          :class:`numpy.ndarray`
              A 4-dimensional array of the same shape as :attr:`batch` with transformation applied.
          """
          if self.is_display:
              return batch
          logger.trace("Randomly transforming image")
          rotation_range = self._config.get("rotation_range", 10)
          zoom_range = self._config.get("zoom_amount", 5) / 100
          shift_range = self._config.get("shift_range", 5) / 100
  
          rotation = np.random.uniform(-rotation_range,
                                       rotation_range,
                                       size=self._batchsize).astype("float32")
          scale = np.random.uniform(1 - zoom_range,
                                    1 + zoom_range,
                                    size=self._batchsize).astype("float32")
          tform = np.random.uniform(
 
</s> remove         scale = np.random.uniform(1 - zoom_range,
                                  1 + zoom_range,
 </s> add         scale = np.random.uniform(1 - self._constants.transform_zoom,
                                  1 + self._constants.transform_zoom, </s> remove 
        Returns
        ----------
        :class:`numpy.ndarray`
            A 4-dimensional array of the same shape as :attr:`batch` with transformation applied.
 </s> add  </s> remove         tform = np.random.uniform(
            -shift_range,
            shift_range,
            size=(self._batchsize, 2)).astype("float32") * self._training_size
 </s> add  </s> remove 
        Returns
        ----------
        :class:`numpy.ndarray`
            A 4-dimensional array of the same shape as :attr:`batch` with transformation applied.
 </s> add  </s> remove         if not self.is_display:
            logger.trace("Randomly flipping image")
            randoms = np.random.rand(self._batchsize)
            indices = np.where(randoms > self._config.get("random_flip", 50) / 100)[0]
            batch[indices] = batch[indices, :, ::-1]
            logger.trace("Randomly flipped %s images of %s", len(indices), self._batchsize)
        return batch

    def warp(self, batch, to_landmarks=False, **kwargs):
 </s> add         logger.trace("Randomly flipping image")  # type: ignore
        randoms = np.random.rand(self._batchsize)
        indices = np.where(randoms > int(self._config.get("random_flip", 50)) / 100)[0]
        batch[indices] = batch[indices, :, ::-1]
        logger.trace("Randomly flipped %s images of %s",  # type: ignore
                     len(indices), self._batchsize)

    def warp(self, batch: np.ndarray, to_landmarks: bool = False, **kwargs) -> np.ndarray:
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
          ----------
          :class:`numpy.ndarray`
              A 4-dimensional array of the same shape as :attr:`batch` with transformation applied.
          """
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
        logger.trace("Randomly transforming image")  # type: ignore

        rotation = np.random.uniform(-self._constants.transform_rotation,
                                     self._constants.transform_rotation,
                                       size=self._batchsize).astype("float32")
          scale = np.random.uniform(1 - zoom_range,
                                    1 + zoom_range,
                                    size=self._batchsize).astype("float32")
          tform = np.random.uniform(
keep keep keep keep replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Data Augmentation update (#1263)

- lib.detected_face
    - Subclass Masks for Landmark based masks
    - Add training mask propery + methods to DetectedFace
  - lib.training_training
    - subclass TrainingDataGenerator for training and preview data
    - Split cache into own module
    - Reduce thread count to 1 to prevent image corruption + data re-use
    - Process on largest model input/output size rather than stored image size
    - Size and crop masks during caching stage
    - Implement ring buffer for data flow
    - Fix preview reload bug
  - augmentation
    - typing
    - switch color aug order
    - better initialization
    - Fix warp + landmark warp to correctly apply at different image scales
    - Slightly improved warp caching
    - Don't store whether image is_preview. Handle all data as training images implicitly
  - plugins.trainer: Typing and fixes to work with trainingdata refactor 
lib/training/augmentation.py