code: def test_plot_method_curve_metrics(
      metric_class, preds, target, thresholds, score
  ):
      """Test that the plot method works for metrics that plot curve objects."""
      metric = metric_class(thresholds=thresholds)
      metric.update(preds(), target())
      fig, ax = metric.plot(score=score)
      assert isinstance(fig, plt.Figure)
      assert isinstance(ax, matplotlib.axes.Axes)
      plt.close(fig)
  
imports: import sys
  from functools import partial
  from typing import Callable
  import matplotlib
  import matplotlib.pyplot
  import numpy
  import pytest
  import torch
  from torch import tensor
  from torchmetrics import MetricCollection
  from torchmetrics.aggregation import MaxMetric, MeanMetric, MinMetric, SumMetric
  from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio, ScaleInvariantSignalDistortionRatio, ScaleInvariantSignalNoiseRatio, ShortTimeObjectiveIntelligibility, SignalDistortionRatio, SignalNoiseRatio
  from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
  from torchmetrics.audio.pit import PermutationInvariantTraining
  from torchmetrics.audio.srmr import SpeechReverberationModulationEnergyRatio
  from torchmetrics.classification import BinaryAccuracy, BinaryAUROC, BinaryAveragePrecision, BinaryCalibrationError, BinaryCohenKappa, BinaryConfusionMatrix, BinaryF1Score, BinaryFairness, BinaryFBetaScore, BinaryHammingDistance, BinaryHingeLoss, BinaryJaccardIndex, BinaryMatthewsCorrCoef, BinaryPrecision, BinaryPrecisionRecallCurve, BinaryRecall, BinaryRecallAtFixedPrecision, BinaryROC, BinarySpecificity, Dice, MulticlassAccuracy, MulticlassAUROC, MulticlassAveragePrecision, MulticlassCalibrationError, MulticlassCohenKappa, MulticlassConfusionMatrix, MulticlassExactMatch, MulticlassF1Score, MulticlassFBetaScore, MulticlassHammingDistance, MulticlassHingeLoss, MulticlassJaccardIndex, MulticlassMatthewsCorrCoef, MulticlassPrecision, MulticlassPrecisionRecallCurve, MulticlassRecall, MulticlassRecallAtFixedPrecision, MulticlassROC, MulticlassSpecificity, MultilabelAveragePrecision, MultilabelConfusionMatrix, MultilabelCoverageError, MultilabelExactMatch, MultilabelF1Score, MultilabelFBetaScore, MultilabelHammingDistance, MultilabelJaccardIndex, MultilabelMatthewsCorrCoef, MultilabelPrecision, MultilabelPrecisionRecallCurve, MultilabelRankingAveragePrecision, MultilabelRankingLoss, MultilabelRecall, MultilabelRecallAtFixedPrecision, MultilabelROC, MultilabelSpecificity
  from torchmetrics.clustering import AdjustedRandScore, CalinskiHarabaszScore, DunnIndex, MutualInfoScore, NormalizedMutualInfoScore, RandScore
  from torchmetrics.detection import PanopticQuality
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
  from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis, FrechetInceptionDistance, InceptionScore, KernelInceptionDistance, LearnedPerceptualImagePatchSimilarity, MemorizationInformedFrechetInceptionDistance, MultiScaleStructuralSimilarityIndexMeasure, PeakSignalNoiseRatio, RelativeAverageSpectralError, RootMeanSquaredErrorUsingSlidingWindow, SpectralAngleMapper, SpectralDistortionIndex, StructuralSimilarityIndexMeasure, TotalVariation, UniversalImageQualityIndex
  from torchmetrics.nominal import CramersV, FleissKappa, PearsonsContingencyCoefficient, TheilsU, TschuprowsT
  from torchmetrics.regression import ConcordanceCorrCoef, CosineSimilarity, ExplainedVariance, KendallRankCorrCoef, KLDivergence, LogCoshError, MeanAbsoluteError, MeanAbsolutePercentageError, MeanSquaredError, MeanSquaredLogError, MinkowskiDistance, PearsonCorrCoef, R2Score, RelativeSquaredError, SpearmanCorrCoef, SymmetricMeanAbsolutePercentageError, TweedieDevianceScore, WeightedMeanAbsolutePercentageError
  from torchmetrics.retrieval import RetrievalFallOut, RetrievalHitRate, RetrievalMAP, RetrievalMRR, RetrievalNormalizedDCG, RetrievalPrecision, RetrievalPrecisionRecallCurve, RetrievalRecall, RetrievalRecallAtFixedPrecision, RetrievalRPrecision
  from torchmetrics.text import BERTScore, BLEUScore, CharErrorRate, CHRFScore, EditDistance, ExtendedEditDistance, InfoLM, MatchErrorRate, Perplexity, ROUGEScore, SacreBLEUScore, SQuAD, TranslationEditRate, WordErrorRate, WordInfoLost, WordInfoPreserved
  from torchmetrics.utilities.imports import _TORCHAUDIO_GREATER_EQUAL_0_10
  from torchmetrics.utilities.plot import _get_col_row_split
  from torchmetrics.wrappers import BootStrapper, ClasswiseWrapper, MetricTracker, MinMaxMetric, MultioutputWrapper, Running
label_desc: Use image node_id 8 for calling a global function with example usage: test_plot_method_curve_metrics(metric_class, preds, target, thresholds, score) without return types