code: def test_cast_genotypes_float32(spark, rg):
      num_samples = 50
      genotype_df = pd.DataFrame(rg.integers(0, 10, (num_samples, 10)))
      phenotype_df = pd.DataFrame(
          random_phenotypes((num_samples, 5), rg)
      )
      covariate_df = pd.DataFrame(rg.random((num_samples, 5)))
      baseline = statsmodels_baseline(
          genotype_df, phenotype_df, covariate_df
      )
      results = run_logistic_regression_spark(
          spark, genotype_df, phenotype_df, covariate_df, dt=np.float32
      )
      assert results["pvalue"].dtype == np.float32
      assert regression_results_equal(baseline, results, rtol=1e-4)
  
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 21 for calling a global function with example usage: test_cast_genotypes_float32(spark, rg) without return types