class_docstr: Test class for `MulticlassSpecificityAtSensitivity` metric.
class_docstr_tok: ["Test","class","for","`","MulticlassSpecificityAtSensitivity","`","metric","."]
docstr: Test dtype support of the metric on GPU.
code: def test_multiclass_specificity_at_sensitivity_dtype_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=MulticlassSpecificityAtSensitivity,
          metric_functional=multiclass_specificity_at_sensitivity,
          metric_args={
              "min_sensitivity": 0.5,
              "thresholds": None,
              "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 roc_curve
  from torchmetrics.classification.specificity_sensitivity import BinarySpecificityAtSensitivity, MulticlassSpecificityAtSensitivity, MultilabelSpecificityAtSensitivity, SpecificityAtSensitivity
  from torchmetrics.functional.classification.specificity_sensitivity import _convert_fpr_to_specificity, binary_specificity_at_sensitivity, multiclass_specificity_at_sensitivity, multilabel_specificity_at_sensitivity
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES
  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 5 for calling the TestMulticlassSpecificityAtSensitivity obj's underlying member method code with example usage: obj.test_multiclass_specificity_at_sensitivity_dtype_gpu(inputs, dtype) without return types