code: def test_missing_and_simple_offset_out_of_order_with_intersect(
      spark, rg
  ):
      n_sample = 50
      n_cov = 5
      n_pheno = 5
      num_geno = 10
      genotype_df = pd.DataFrame(rg.random((n_sample, num_geno)))
      # here we're simulating dropping samples from phenotypes and covariates
      phenotype_df = pd.DataFrame(
          random_phenotypes((n_sample, n_pheno), rg)
      )[0:-2]
      phenotype_df.loc[[1, 3, 5], 1] = np.nan
      covariate_df = pd.DataFrame(rg.random((n_sample, n_cov)))[0:-2]
      offset_df = pd.DataFrame(rg.random((n_sample, n_pheno)))
      glow = run_logistic_regression_spark(
          spark,
          genotype_df,
          phenotype_df,
          covariate_df,
          offset_df=offset_df.sample(frac=1),
          intersect_samples=True,
          genotype_sample_ids=genotype_df.index.values.astype(
              str
          ).tolist(),
      )
      # drop samples from genotypes so that input samples are aligned
      baseline = statsmodels_baseline(
          genotype_df[0:-2],
          phenotype_df,
          covariate_df,
          [offset_df[0:-2]] * num_geno,
      )
      assert regression_results_equal(glow, baseline)
  
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 18 for calling a global function with example usage: test_missing_and_simple_offset_out_of_order_with_intersect(spark, rg) without return types