class_docstr: Test class for `BinaryCohenKappa` metric.
class_docstr_tok: ["Test","class","for","`","BinaryCohenKappa","`","metric","."]
docstr: Test the differentiability of the metric, according to its `is_differentiable` attribute.
docstr_tok: ["Test","the","differentiability","of","the","metric",",","according","to","its","`","is_differentiable","`","attribute","."]
code: def test_binary_cohen_kappa_differentiability(self, inputs):
      """Test the differentiability of the metric, according to its `is_differentiable` attribute."""
      preds, target = inputs
      self.run_differentiability_test(
          preds=preds,
          target=target,
          metric_module=BinaryCohenKappa,
          metric_functional=binary_cohen_kappa,
          metric_args={"threshold": THRESHOLD},
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import cohen_kappa_score
  from torchmetrics.classification.cohen_kappa import BinaryCohenKappa, CohenKappa, MulticlassCohenKappa
  from torchmetrics.functional.classification.cohen_kappa import binary_cohen_kappa, multiclass_cohen_kappa
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _multiclass_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 3 for calling the TestBinaryCohenKappa obj's underlying member method code with example usage: obj.test_binary_cohen_kappa_differentiability(inputs) without return types