code: def _sklearn_deviance(preds: Tensor, targets: Tensor, power: float):
      sk_preds = preds.view(-1).numpy()
      sk_target = targets.view(-1).numpy()
      return mean_tweedie_deviance(sk_target, sk_preds, power=power)
  
imports: from functools import partial
  import pytest
  import torch
  from sklearn.metrics import mean_tweedie_deviance
  from torch import Tensor
  from torchmetrics.functional.regression.tweedie_deviance import tweedie_deviance_score
  from torchmetrics.regression.tweedie_deviance import TweedieDevianceScore
  from unittests import BATCH_SIZE, NUM_BATCHES, _Input
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 1 for calling a global function with example usage: _sklearn_deviance(preds, targets, power) and returns: mean_tweedie_deviance