code: def _true_negative_rate_per_class(
      y_true, y_pred, classes
  ):  # True Negatives / (True Negatives + False Positives)
      result = []
      for cls in classes:
          y_true_cls, y_pred_cls = (
              np.asarray(y_true) == cls,
              np.asarray(y_pred) == cls,
          )
          matrix = confusion_matrix(
              y_true_cls, y_pred_cls, labels=[0, 1]
          )
          result.append(
              matrix[1, 1] / (matrix[1, 1] + matrix[0, 1])
              if (matrix[1, 1] + matrix[0, 1]) > 0
              else 0
          )
      return np.asarray(result)
  
file_path: deepchecks/deepchecks/tabular/metric_utils/additional_classification_metrics.py
imports: from typing import Union
  import numpy
  from sklearn.metrics import confusion_matrix, roc_auc_score
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.utils.metrics import averaging_mechanism
label_desc: Use image node_id 10 for calling a global function with example usage: _true_negative_rate_per_class(y_true, y_pred, classes) and returns: np