code: def _sklearn_hamming_loss(target, preds):
      score = sk_hamming_loss(target, preds)
      return score if not np.isnan(score) else 1.0
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import hamming_loss
  from torchmetrics.classification.hamming import BinaryHammingDistance, HammingDistance, MulticlassHammingDistance, MultilabelHammingDistance
  from torchmetrics.functional.classification.hamming import binary_hamming_distance, multiclass_hamming_distance, multilabel_hamming_distance
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 1 for calling a global function with example usage: _sklearn_hamming_loss(target, preds) and returns: unknown