class_docstr: Test class for `MulticlassPrecisionRecallCurve` metric.
class_docstr_tok: ["Test","class","for","`","MulticlassPrecisionRecallCurve","`","metric","."]
docstr: Test dtype support of the metric on GPU.
docstr_tok: ["Test","dtype","support","of","the","metric","on","GPU","."]
code: def test_multiclass_precision_recall_curve_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=MulticlassPrecisionRecallCurve,
          metric_functional=multiclass_precision_recall_curve,
          metric_args={"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 precision_recall_curve
  from torchmetrics.classification.precision_recall_curve import BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve, PrecisionRecallCurve
  from torchmetrics.functional.classification.precision_recall_curve import binary_precision_recall_curve, multiclass_precision_recall_curve, multilabel_precision_recall_curve
  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 TestMulticlassPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_multiclass_precision_recall_curve_dtype_gpu(inputs, dtype) without return types