code: def link_functions_tweedie_vpow():
      np.random.seed(1234)
      n_rows = 10
  
      data = {
          "X1": np.random.randn(n_rows),
          "X2": np.random.randn(n_rows),
          "X3": np.random.randn(n_rows),
          "W": np.random.choice([10, 20], size=n_rows),
          "Y": np.random.choice(
              [0, 0, 0, 0, 0, 10, 20, 30], size=n_rows
          ),
      }
  
      train = h2o.H2OFrame(pd.DataFrame(data))
      test = train.drop("W")
      print(train)
      h2o_model = H2OGeneralizedAdditiveEstimator(
          family="tweedie",
          gam_columns=["X3"],
          weights_column="W",
          lambda_=0,
          tweedie_variance_power=1.5,
          tweedie_link_power=0,
      )
      h2o_model.train(x=["X1", "X2"], y="Y", training_frame=train)
  
      predict_w = h2o_model.predict(train)
      predict = h2o_model.predict(test)  # scoring without weight column
      # should produce same frame
      pyunit_utils.compare_frames_local(
          predict_w, predict, prob=1, tol=1e-6
      )
  
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