code: def classification_metrics(
      y, p, w=None, metrics={"AUC": roc_auc_score, "Log Loss": logloss}
  ):
      """Log metrics for classifiers.
  
      Args:
          y (numpy.array): target
          p (numpy.array): prediction
          w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log
              metrics for the treatment and control group separately
          metrics (dict, optional): a dictionary of the metric names and functions
      """
      regression_metrics(y=y, p=p, w=w, metrics=metrics)
  
imports: import logging
  from sklearn.metrics import log_loss, roc_auc_score
  from .const import EPS
  from .regression import regression_metrics
label_desc: Use image node_id 2 for calling a global function with example usage: classification_metrics(y, p, w, metrics) without return types