code: def _baseline_specificity_multiclass(
      preds, target, ignore_index, multidim_average, average
  ):
      if preds.ndim == target.ndim + 1:
          preds = torch.argmax(preds, 1)
      if multidim_average == "global":
          return _baseline_specificity_multiclass_global(
              preds, target, ignore_index, average
          )
      return _baseline_specificity_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 torch import Tensor, tensor
  from torchmetrics.classification.specificity import BinarySpecificity, MulticlassSpecificity, MultilabelSpecificity, Specificity
  from torchmetrics.functional.classification.specificity import binary_specificity, multiclass_specificity, multilabel_specificity
  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
label_desc: Use image node_id 5 for calling a global function with example usage: _baseline_specificity_multiclass(preds, target, ignore_index, multidim_average, average) and returns: _baseline_specificity_multiclass_local, _baseline_specificity_multiclass_global