code: def test_regression_generate_alphas(spark):
      indexdf = spark.read.parquet(
          f"{data_root}/groupedIDs.snappy.parquet"
      )
      level1df = spark.read.parquet(
          f"{data_root}/level1BlockedGT.snappy.parquet"
      ).limit(5)
      group2ids = __get_sample_blocks(indexdf)
  
      regressor_without_alphas = RidgeRegression(
          level1df, std_labeldf, group2ids, add_intercept=False
      )
      regressor_with_alphas = RidgeRegression(
          level1df,
          std_labeldf,
          group2ids,
          add_intercept=False,
          alphas=sorted(list(generate_alphas(level1df).values())),
      )
  
      (
          model1_without_alphas,
          cv_without_alphas,
      ) = regressor_without_alphas.fit()
      model1df, cvdf = regressor_with_alphas.fit()
      __assert_dataframes_equal(model1_without_alphas, model1df)
      __assert_dataframes_equal(cv_without_alphas, cvdf)
  
      yhat_without_alphas = regressor_without_alphas.transform()
      yhatdf = regressor_with_alphas.transform()
      assert yhat_without_alphas.equals(yhatdf)
  
imports: from glow.wgr.ridge_reduction import *
  from glow.wgr.ridge_regression import *
  from glow.wgr.ridge_udfs import *
  from glow.wgr.model_functions import _fill_na_and_standardize
  import numpy
  import pandas
  from pyspark.sql.functions import *
  import math
label_desc: Use image node_id 18 for calling a global function with example usage: test_regression_generate_alphas(spark) without return types