code: def _micro_false_negative_rate(y_true, y_pred, classes):
      fn, tp = 0, 0
      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]
          )
          fn += matrix[1, 0]
          tp += matrix[0, 0]
      return fn / (fn + tp) if (fn + tp) > 0 else 0
  
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 8 for calling a global function with example usage: _micro_false_negative_rate(y_true, y_pred, classes) and returns: unknown