text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from pytorch_lightning.metrics.functional.classification import (
      accuracy,
      auc,
      auroc,
      average_precision,
      dice_score,
      multiclass_precision_recall_curve,
      multiclass_roc,
      multiclass_auroc,
      precision,
      precision_recall,
      precision_recall_curve,
      recall,
      roc,
      stat_scores,
      stat_scores_multiple_classes,
      to_categorical,
      to_onehot,
      iou,
  )
  from pytorch_lightning.metrics.functional.nlp import bleu_score
  from pytorch_lightning.metrics.functional.self_supervised import (
      embedding_similarity
  )
  # TODO: unify metrics between class and functional, add below
  from pytorch_lightning.metrics.functional.explained_variance import explained_variance
  from pytorch_lightning.metrics.functional.mean_absolute_error import mean_absolute_error
  from pytorch_lightning.metrics.functional.mean_squared_error import mean_squared_error
  from pytorch_lightning.metrics.functional.mean_squared_log_error import mean_squared_log_error
  from pytorch_lightning.metrics.functional.psnr import psnr
  from pytorch_lightning.metrics.functional.ssim import ssim
  from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix
  from pytorch_lightning.metrics.functional.f_beta import fbeta, f1
  