code: def link_functions_tweedie_vpow():
      np.random.seed(25)
  
      data = {
          "predictor": np.random.uniform(400, 800, 15),
          "target": np.random.uniform(0.7, 1.4, 15),
          "weight_1": [1] * 15,
          "weight_2": [3] * 15,
      }
  
      df = h2o.H2OFrame(pd.DataFrame(data))
  
      model_w1 = H2OGeneralizedAdditiveEstimator(
          family="gaussian",
          gam_columns=["predictor"],
          scale=[1],
          bs=[0],
          weights_column="weight_1",
      )
      model_w2 = H2OGeneralizedAdditiveEstimator(
          family="gaussian",
          gam_columns=["predictor"],
          scale=[1],
          bs=[0],
          weights_column="weight_2",
      )
      model = H2OGeneralizedAdditiveEstimator(
          family="gaussian",
          gam_columns=["predictor"],
          scale=[1],
          bs=[0],
      )
  
      model_w1.train(x=["predictor"], y="target", training_frame=df)
      model_w2.train(x=["predictor"], y="target", training_frame=df)
      model.train(x=["predictor"], y="target", training_frame=df)
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
  import pandas
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: link_functions_tweedie_vpow() without return types