code: def _micro_false_positive_rate(y_true, y_pred, classes):
      fp, tn = 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]
          )
          fp += matrix[0, 1]
          tn += matrix[1, 1]
      return fp / (fp + tn) if (fp + tn) > 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 5 for calling a global function with example usage: _micro_false_positive_rate(y_true, y_pred, classes) and returns: unknown