class_docstr: Wrap metric to fit testing framework.
docstr: Instead of returning dict, return as list.
docstr_tok: ["Instead","of","returning","dict",",","return","as","list","."]
code: def compute(self):
      """Instead of returning dict, return as list."""
      output_dict = super().compute()
      return [
          output_dict["raw"],
          output_dict["min"],
          output_dict["max"],
      ]
  
imports: from copy import deepcopy
  from functools import partial
  from typing import Any
  import pytest
  import torch
  from torch import Tensor
  from torchmetrics.classification import BinaryAccuracy, BinaryConfusionMatrix, MulticlassAccuracy
  from torchmetrics.regression import MeanSquaredError
  from torchmetrics.wrappers import MinMaxMetric
  from unittests import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 1 for calling the TestingMinMaxMetric obj's underlying member method code with example usage: obj.compute() and returns: list