code: def test_tweedie_p_and_phi_estimation_3_no_link_power_est():
      training_data = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/glm_test/tweedie_p3_phi1_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=False,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=2.5,
          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 = 3, phi = 1 converged to p =",
          model_ml.actual_params["tweedie_variance_power"],
          "; phi =",
          model_ml.actual_params["init_dispersion_parameter"],
      )
      # p = 3, phi = 1 converged to p = 2.9892180905617645 ; phi = 0.9918345933463155
      # Estimate from R tweedie.profile (using p with step 0.001): p= 2.988776 ;  phi= 0.991949
      assert (
          abs(
              model_ml.actual_params["tweedie_variance_power"] - 2.98921
          )
          < 2e-4
      )
      assert (
          abs(
              model_ml.actual_params["init_dispersion_parameter"]
              - 0.9918
          )
          < 2e-4
      )
  
file_path: h2o-3/h2o-py/tests/testdir_algos/glm/pyunit_tweedie_p_and_phi_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 2 for calling a global function with example usage: test_tweedie_p_and_phi_estimation_3_no_link_power_est() without return types