code: def test_reducer_generate_alphas(spark):
      indexdf = spark.read.parquet(
          f"{data_root}/groupedIDs.snappy.parquet"
      )
      blockdf = spark.read.parquet(
          f"{data_root}/blockedGT.snappy.parquet"
      ).limit(5)
      group2ids = __get_sample_blocks(indexdf)
  
      stack_without_alphas = RidgeReduction(
          blockdf, std_labeldf, group2ids, add_intercept=False
      )
      stack_with_alphas = RidgeReduction(
          blockdf,
          std_labeldf,
          group2ids,
          add_intercept=False,
          alphas=sorted(list(generate_alphas(blockdf).values())),
      )
  
      model0_without_alphas = stack_without_alphas.fit()
      model0df = stack_with_alphas.fit()
      __assert_dataframes_equal(model0_without_alphas, model0df)
  
      level1_without_alphas = stack_without_alphas.transform()
      level1df = stack_with_alphas.transform()
      __assert_dataframes_equal(level1_without_alphas, level1df)
  
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 17 for calling a global function with example usage: test_reducer_generate_alphas(spark) without return types