code: def gradient(self, y, f):
      error = y - f
      # smaller predicted sales value is better error than the bigger one
      return 10 * error if error < 0 else 0.5 * error
  
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 1 for calling the AsymmetricLossDistribution obj's underlying member method code with example usage: obj.gradient(y, f) and returns: unknown