code: def test_quantile(self):
      pser = pd.Series([])
      psser = ps.from_pandas(pser)
  
      self.assert_eq(psser.quantile(0.5), pser.quantile(0.5))
      self.assert_eq(
          psser.quantile([0.25, 0.5, 0.75]),
          pser.quantile([0.25, 0.5, 0.75]),
      )
  
      with self.assertRaisesRegex(
          TypeError, "accuracy must be an integer; however"
      ):
          ps.Series([24.0, 21.0, 25.0, 33.0, 26.0]).quantile(
              accuracy="a"
          )
      with self.assertRaisesRegex(
          TypeError, "q must be a float or an array of floats;"
      ):
          ps.Series([24.0, 21.0, 25.0, 33.0, 26.0]).quantile(q=1)
      with self.assertRaisesRegex(
          TypeError, "q must be a float or an array of floats;"
      ):
          ps.Series([24.0, 21.0, 25.0, 33.0, 26.0]).quantile(q=["a"])
      with self.assertRaisesRegex(
          ValueError,
          "percentiles should all be in the interval \\[0, 1\\]",
      ):
          ps.Series([24.0, 21.0, 25.0, 33.0, 26.0]).quantile(q=1.1)
  
      with self.assertRaisesRegex(
          TypeError, "Could not convert object \\(string\\) to numeric"
      ):
          ps.Series(["a", "b", "c"]).quantile()
      with self.assertRaisesRegex(
          TypeError, "Could not convert object \\(string\\) to numeric"
      ):
          ps.Series(["a", "b", "c"]).quantile([0.25, 0.5, 0.75])
  
imports: import unittest
  from decimal import Decimal
  import numpy
  import pandas
  from pyspark import pandas
  from pyspark.testing.pandasutils import ComparisonTestBase
  from pyspark.testing.sqlutils import SQLTestUtils
label_desc: Use image node_id 9 for calling the SeriesStatMixin obj's underlying member method code with example usage: obj.test_quantile() without return types