code: def weights_and_distributions():
      htable = h2o.upload_file(
          pyunit_utils.locate("smalldata/gbm_test/moppe.csv")
      )
      htable["premiekl"] = htable["premiekl"].asfactor()
      htable["moptva"] = htable["moptva"].asfactor()
      htable["zon"] = htable["zon"]
  
      # gamma
      dl = H2ODeepLearningEstimator(distribution="gamma")
      dl.train(
          x=list(range(3)),
          y="medskad",
          training_frame=htable,
          weights_column="antskad",
      )
      predictions = dl.predict(htable)
  
      # gaussian
      dl = H2ODeepLearningEstimator(distribution="gaussian")
      dl.train(
          x=list(range(3)),
          y="medskad",
          training_frame=htable,
          weights_column="antskad",
      )
      predictions = dl.predict(htable)
  
      # poisson
      dl = H2ODeepLearningEstimator(distribution="poisson")
      dl.train(
          x=list(range(3)),
          y="medskad",
          training_frame=htable,
          weights_column="antskad",
      )
      predictions = dl.predict(htable)
  
      # tweedie
      dl = H2ODeepLearningEstimator(distribution="tweedie")
      dl.train(
          x=list(range(3)),
          y="medskad",
          training_frame=htable,
          weights_column="antskad",
      )
      predictions = dl.predict(htable)
  
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: weights_and_distributions() without return types