code: def test_tweedie_var_power_estimation_3_phi_0p5_no_link_power_est():
      training_data = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/glm_test/tweedie_p3_phi0p5_10KRows.csv"
          )
      )
      Y = "x"
      x = ["abs.C1.", "abs.C2.", "abs.C3.", "abs.C4.", "abs.C5."]
      training_data = training_data[training_data[Y] > 0, :]
      model_ml = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=2.5,
          init_dispersion_parameter=0.5,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          seed=12345,
      )
      model_ml.train(training_frame=training_data, x=x, y=Y)
      print(model_ml.actual_params["tweedie_variance_power"])
      # assert abs(model_ml.actual_params["tweedie_variance_power"] - 3.029819714027204) < 0.001
      assert (
          abs(
              model_ml.actual_params["tweedie_variance_power"]
              - 3.0632864030533775
          )
          < 0.001
      )
  
      model_ml = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=1.5,
          init_dispersion_parameter=0.5,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          seed=12345,
      )
      model_ml.train(training_frame=training_data, x=x, y=Y)
      print(model_ml.actual_params["tweedie_variance_power"])
      # assert abs(model_ml.actual_params["tweedie_variance_power"] - 3.014297969690198) < 0.001
      assert (
          abs(
              model_ml.actual_params["tweedie_variance_power"]
              - 3.0064318296956998
          )
          < 0.001
      )
  
file_path: h2o-3/h2o-py/tests/testdir_algos/glm/pyunit_tweedie_variance_power_ml_estimation_large.py
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 5 for calling a global function with example usage: test_tweedie_var_power_estimation_3_phi_0p5_no_link_power_est() without return types