code: def evaluate(test, predictions):
      predictions["actual"] = test.sales.values
      predictions.columns = ["prediction", "actual"]
      predictions["residual"] = (
          predictions.actual - predictions.prediction
      )
      predictions["fit"] = 0
      predictions.loc[predictions.residual < 0, "fit"] = 0
      predictions.loc[predictions.residual >= 0, "fit"] = 1
      items = predictions.shape[0]
      more_or_perfect = sum(predictions.fit)
      less = items - more_or_perfect
      return items, less, more_or_perfect
  
file_path: h2o-3/h2o-py/tests/testdir_algos/gbm/pyunit_PUBDEV_4076_custom_distribution_demo.py
imports: import sys
  import h2o
  from tests import pyunit_utils
  import pandas
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.utils.distributions import CustomDistributionGaussian
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(test, predictions) and returns: items,  less,  more_or_perfect