code: def _sklearn_stat_scores_multiclass(
      preds, target, ignore_index, multidim_average, average
  ):
      if preds.ndim == target.ndim + 1:
          preds = torch.argmax(preds, 1)
      if multidim_average == "global":
          return _sklearn_stat_scores_multiclass_global(
              preds, target, ignore_index, average
          )
      return _sklearn_stat_scores_multiclass_local(
          preds, target, ignore_index, average
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import confusion_matrix
  from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores, StatScores
  from torchmetrics.functional.classification.stat_scores import binary_stat_scores, multiclass_stat_scores, multilabel_stat_scores
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  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 4 for calling a global function with example usage: _sklearn_stat_scores_multiclass(preds, target, ignore_index, multidim_average, average) and returns: _sklearn_stat_scores_multiclass_local, _sklearn_stat_scores_multiclass_global