code: def test_tweedie_var_power_estimation_2p6_disp2_est():
      training_data = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/glm_test/tweedie_p2p6_disp2_5Cols_10krows_est1p94.csv"
          )
      )
      Y = "resp"
      x = ["abs.C1.", "abs.C2.", "abs.C3.", "abs.C4.", "abs.C5."]
      training_data = training_data[training_data[Y] > 0, :]
      trueDisp = 2.6
      model_ml = H2OGeneralizedLinearEstimator(
          family="tweedie",
          fix_dispersion_parameter=True,
          fix_tweedie_variance_power=False,
          tweedie_variance_power=trueDisp,
          lambda_=0,
          compute_p_values=False,
          init_dispersion_parameter=2.0,
          dispersion_parameter_method="ml",
          seed=12345,
      )
      model_ml.train(training_frame=training_data, x=x, y=Y)
      print(
          "estimated variance power: {0}, true variance power: {1}".format(
              model_ml.actual_params["tweedie_variance_power"], trueDisp
          )
      )
      assert (
          abs(
              model_ml.actual_params["tweedie_variance_power"]
              - 2.67437207518084
          )
          < 2e-4
      )
  
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 8 for calling a global function with example usage: test_tweedie_var_power_estimation_2p6_disp2_est() without return types