code: def test_dice(pred, target, expected):
      """Test that implementation returns the correct result."""
      score = dice(tensor(pred), tensor(target), ignore_index=0)
      assert score == expected
  
imports: from functools import partial
  from typing import Optional
  import pytest
  from scipy.spatial.distance import dice
  from torch import Tensor, tensor
  from torchmetrics.classification import Dice
  from torchmetrics.functional import dice
  from torchmetrics.functional.classification.stat_scores import _del_column
  from torchmetrics.utilities.checks import _input_format_classification
  from torchmetrics.utilities.enums import DataType
  from unittests.classification.inputs import _input_binary, _input_binary_logits, _input_binary_prob
  from unittests.classification.inputs import _input_multiclass
  from unittests.classification.inputs import _input_multiclass_logits
  from unittests.classification.inputs import _input_multiclass_prob
  from unittests.classification.inputs import _input_multiclass_with_missing_class
  from unittests.classification.inputs import _input_multilabel
  from unittests.classification.inputs import _input_multilabel_logits
  from unittests.classification.inputs import _input_multilabel_multidim
  from unittests.classification.inputs import _input_multilabel_multidim_prob
  from unittests.classification.inputs import _input_multilabel_prob
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 2 for calling a global function with example usage: test_dice(pred, target, expected) without return types