code: def test_tweedie_var_power_estimation_1p2_phi_2_no_link_power_est():
      training_data = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/tweedie_p1p2_phi2_5Cols_10KRows.csv"
          )
      )
      Y = "resp"
      x = ["abs.C1.", "abs.C2.", "abs.C3.", "abs.C4.", "abs.C5."]
      model_ml = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=2.5,
          init_dispersion_parameter=2,
          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"]
              - 1.1975634809085063
          )
          < 0.001
      )
      # assert abs(model_ml.actual_params["tweedie_variance_power"] - 1.2671241774599327) < 0.001
  
      model_ml = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=1.5,
          init_dispersion_parameter=2,
          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"]
              - 1.1956740204906753
          )
          < 0.001
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_tweedie_var_power_estimation_1p2_phi_2_no_link_power_est() without return types