code: def test_tweedie_var_power_estimation_1_8_no_link_power_est():
      training_data = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/tweedie_1p8Power_2Dispersion_5Col_10KRows.csv"
          )
      )
      Y = "resp"
      x = ["abs.C1.", "abs.C2.", "abs.C3.", "abs.C4.", "abs.C5."]
      model_18 = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=1.8,
          dispersion_learning_rate=1,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          init_dispersion_parameter=2,
          max_iterations_dispersion=1000,
      )
      model_18.train(training_frame=training_data, x=x, y=Y)
      print(
          "p = 1.8 converged to p =",
          model_18.actual_params["tweedie_variance_power"],
      )
      assert (
          abs(
              model_18.actual_params["tweedie_variance_power"]
              - 1.7988226798952265
          )
          < 1e-4
      )
  
      model_11 = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=1.1,
          dispersion_learning_rate=1,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          init_dispersion_parameter=2,
          max_iterations_dispersion=1000,
      )
      model_11.train(training_frame=training_data, x=x, y=Y)
      print(
          "p = 1.1 converged to p =",
          model_11.actual_params["tweedie_variance_power"],
      )
      assert (
          abs(
              model_11.actual_params["tweedie_variance_power"]
              - model_18.actual_params["tweedie_variance_power"]
          )
          < 1e-3
      )
  
      model_201 = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=2.01,
          dispersion_learning_rate=1,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          init_dispersion_parameter=2,
          max_iterations_dispersion=1000,
      )
      model_201.train(training_frame=training_data, x=x, y=Y)
      print(
          "p = 2.01 converged to p =",
          model_201.actual_params["tweedie_variance_power"],
      )
      assert (
          abs(
              model_11.actual_params["tweedie_variance_power"]
              - model_201.actual_params["tweedie_variance_power"]
          )
          < 1e-3
      )
  
      model_21 = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=2.1,
          dispersion_learning_rate=1,
          lambda_=0,
          compute_p_values=False,
          dispersion_parameter_method="ml",
          init_dispersion_parameter=2,
          max_iterations_dispersion=1000,
      )
      model_21.train(training_frame=training_data, x=x, y=Y)
      print(
          "p = 2.1 converged to p =",
          model_21.actual_params["tweedie_variance_power"],
      )
      assert (
          abs(
              model_11.actual_params["tweedie_variance_power"]
              - model_21.actual_params["tweedie_variance_power"]
          )
          < 1e-3
      )
  
      model_25 = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=2.5,
          dispersion_learning_rate=1,
          lambda_=0,
          compute_p_values=True,
          dispersion_parameter_method="ml",
          init_dispersion_parameter=2,
          max_iterations_dispersion=1000,
      )
      model_25.train(training_frame=training_data, x=x, y=Y)
      print(
          "p = 2.5 converged to p =",
          model_25.actual_params["tweedie_variance_power"],
      )
      assert (
          abs(
              model_11.actual_params["tweedie_variance_power"]
              - model_25.actual_params["tweedie_variance_power"]
          )
          < 1e-3
      )
  
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_var_power_estimation_1_8_no_link_power_est() without return types