class_docstr: Test class for `MulticlassAccuracy` metric.
class_docstr_tok: ["Test","class","for","`","MulticlassAccuracy","`","metric","."]
docstr: Test dtype support of the metric on GPU.
code: def test_multiclass_accuracy_half_gpu(self, inputs, dtype):
      """Test dtype support of the metric on GPU."""
      preds, target = inputs
      self.run_precision_test_gpu(
          preds=preds,
          target=target,
          metric_module=MulticlassAccuracy,
          metric_functional=multiclass_accuracy,
          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 sklearn.metrics import accuracy_score
  from sklearn.metrics import confusion_matrix
  from torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy
  from torchmetrics.functional.classification.accuracy import accuracy, binary_accuracy, multiclass_accuracy, multilabel_accuracy
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _input_binary, _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 5 for calling the TestMulticlassAccuracy obj's underlying member method code with example usage: obj.test_multiclass_accuracy_half_gpu(inputs, dtype) without return types