code: def test_multiple_spark_missing(spark, rg):
      n_sample = 50
      n_cov = 5
      n_pheno = 5
      phenotype_df = pd.DataFrame(
          random_phenotypes((n_sample, n_pheno), rg)
      )
      Y = phenotype_df.to_numpy()
      Y[~random_mask(Y.shape, 10, rg)] = np.nan
      assert phenotype_df.isna().sum().sum() > 0
      covariate_df = pd.DataFrame(rg.random((n_sample, n_cov)))
      genotype_df = pd.DataFrame(rg.random((n_sample, 1)))
      glow = run_logistic_regression_spark(
          spark, genotype_df, phenotype_df, covariate_df
      )
      golden = statsmodels_baseline(
          genotype_df, phenotype_df, covariate_df
      )
      assert regression_results_equal(glow, golden)
  
imports: from pyspark.sql.types import ArrayType, DoubleType
  import glow.gwas.log_reg
  import glow.gwas.functions
  import statsmodels.api
  import pandas
  import numpy
  import pytest
label_desc: Use image node_id 14 for calling a global function with example usage: test_multiple_spark_missing(spark, rg) without return types