code: def test_tweedie_p_and_phi_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=False,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=1.5,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          seed=12345,
      )
      model_ml.train(training_frame=training_data, x=x, y=Y)
      print(
          "p = 1.2, phi = 2 converged to p =",
          model_ml.actual_params["tweedie_variance_power"],
          "; phi =",
          model_ml.actual_params["init_dispersion_parameter"],
      )
      # p = 1.2, phi = 2 converged to p = 1.1932493756749825 ; phi = 2.0191183402978257
      # Estimate from R tweedie.profile (using p with step 0.001; but different link power (R didn't converge with the same as in h2o)): p= 1.195102 ;  phi= 2.032903
      assert (
          abs(
              model_ml.actual_params["tweedie_variance_power"] - 1.19325
          )
          < 0.001
      )
      assert (
          abs(
              model_ml.actual_params["init_dispersion_parameter"]
              - 2.01912
          )
          < 0.001
      )
      assert (
          abs(
              model_ml.actual_params["init_dispersion_parameter"]
              - model_ml._model_json["output"]["dispersion"]
          )
          < 1e-16
      )
  
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_p_and_phi_estimation_1p2_phi_2_no_link_power_est() without return types