  from lib.model.losses import (DSSIMObjective, PenalizedLoss, gradient_loss, mask_loss_wrapper,
                                generalized_loss, l_inf_norm, gmsd_loss, gaussian_blur)
  from lib.model.nn_blocks import NNBlocks
  from lib.model.optimizers import Adam
  from lib.multithreading import MultiThread
  from lib.utils import deprecation_warning, FaceswapError
  from plugins.train._config import Config
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  _CONFIG = None
 
</s> remove from lib.queue_manager import queue_manager
from lib.umeyama import umeyama
from lib.utils import cv2_read_img, FaceswapError
 </s> add from lib.utils import FaceswapError </s> remove from lib.training_data import TrainingDataGenerator, stack_images
 </s> add from lib.training_data import TrainingDataGenerator </s> remove from lib.utils import get_folder, hash_encode_image, deprecation_warning
 </s> add from lib.utils import get_folder, deprecation_warning </s> remove from lib.queue_manager import queue_manager
from lib.utils import cv2_read_img, get_folder, get_image_paths, set_system_verbosity
 </s> add from lib.queue_manager import queue_manager  # noqa pylint:disable=unused-import
from lib.utils import get_folder, get_image_paths, set_system_verbosity
  from lib.model.losses import (DSSIMObjective, PenalizedLoss, gradient_loss, mask_loss_wrapper,
                                generalized_loss, l_inf_norm, gmsd_loss, gaussian_blur)
  from lib.model.nn_blocks import NNBlocks
  from lib.model.optimizers import Adam

  from lib.utils import deprecation_warning, FaceswapError
  from plugins.train._config import Config
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  _CONFIG = None
keep keep keep keep replace keep keep keep keep keep
 Optimize Data Augmentation (#881)

* Move image utils to lib.image
* Add .pylintrc file
* Remove some cv2 pylint ignores
* TrainingData: Load images from disk in batches
* TrainingData: get_landmarks to batch
* TrainingData: transform and flip to batches
* TrainingData: Optimize color augmentation
* TrainingData: Optimize target and random_warp
* TrainingData - Convert _get_closest_match for batching
* TrainingData: Warp To Landmarks optimized
* Save models to threadpoolexecutor
* Move stack_images, Rename ImageManipulation. ImageAugmentation Docstrings
* Masks: Set dtype and threshold for lib.masks based on input face
* Docstrings and Documentation 
plugins/train/model/_base.py