code: def test_verbose_output(spark, rg):
      num_samples = 10
      genotype_df = pd.DataFrame(rg.random((num_samples, 1)))
      phenotype_df = pd.DataFrame(rg.random((num_samples, 3)))
      phenotype_df.loc[0, 0] = np.nan
      phenotype_df.loc[[1, 3, 5], 1] = np.nan
      covariate_df = pd.DataFrame(rg.random((num_samples, 3)))
      glow = run_linear_regression_spark(
          spark,
          genotype_df,
          phenotype_df,
          covariate_df,
          verbose_output=True,
      )
      baseline = statsmodels_baseline(
          genotype_df, phenotype_df, covariate_df
      )
      assert regression_results_equal(
          glow.drop(columns=["n", "sum_x", "y_transpose_x"]), baseline
      )
      assert glow.n.to_list() == [9, 7, 10]
      assert np.allclose(
          glow.y_transpose_x.to_numpy(),
          np.nan_to_num(phenotype_df.to_numpy().T)
          @ genotype_df[0].array,
      )
      assert np.allclose(
          glow.sum_x.to_numpy(),
          (~np.isnan(phenotype_df.to_numpy())).T @ genotype_df[0].array,
      )
  
imports: import functions
  import numpy
  import statsmodels.api
  import pandas
  import glow.gwas.functions
  import glow.gwas.lin_reg
  import pytest
label_desc: Use image node_id 15 for calling a global function with example usage: test_verbose_output(spark, rg) without return types