class_docstr: Test class for `MulticlassJaccardIndex` metric.
docstr: Test the differentiability of the metric, according to its `is_differentiable` attribute.
code: def test_multiclass_jaccard_index_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=MulticlassJaccardIndex,
          metric_functional=multiclass_jaccard_index,
          metric_args={"num_classes": NUM_CLASSES},
      )
  
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 jaccard_score
  from torchmetrics.classification.jaccard import BinaryJaccardIndex, JaccardIndex, MulticlassJaccardIndex, MultilabelJaccardIndex
  from torchmetrics.functional.classification.jaccard import binary_jaccard_index, multiclass_jaccard_index, multilabel_jaccard_index
  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.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 3 for calling the TestMulticlassJaccardIndex obj's underlying member method code with example usage: obj.test_multiclass_jaccard_index_differentiability(inputs) without return types