class_docstr: Test class for `MulticlassHingeLoss` metric.
class_docstr_tok: ["Test","class","for","`","MulticlassHingeLoss","`","metric","."]
docstr: Test dtype support of the metric on CPU.
docstr_tok: ["Test","dtype","support","of","the","metric","on","CPU","."]
code: def test_multiclass_hinge_loss_dtype_cpu(self, inputs, dtype):
      """Test dtype support of the metric on CPU."""
      preds, target = inputs
      if dtype == torch.half:
          pytest.xfail(reason="torch.clamp does not support cpu + half")
      self.run_precision_test_cpu(
          preds=preds,
          target=target,
          metric_module=MulticlassHingeLoss,
          metric_functional=multiclass_hinge_loss,
          metric_args={"num_classes": NUM_CLASSES},
          dtype=dtype,
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from scipy.special import softmax
  from sklearn.metrics import hinge_loss
  from sklearn.preprocessing import OneHotEncoder
  from torchmetrics.classification.hinge import BinaryHingeLoss, HingeLoss, MulticlassHingeLoss
  from torchmetrics.functional.classification.hinge import binary_hinge_loss, multiclass_hinge_loss
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES
  from unittests.classification.inputs import _binary_cases, _multiclass_cases
  from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 4 for calling the TestMulticlassHingeLoss obj's underlying member method code with example usage: obj.test_multiclass_hinge_loss_dtype_cpu(inputs, dtype) without return types