class_docstr: Unit tests for arithmetic operations of numeric data types.
  
  A few test cases are disabled because pandas-on-Spark returns float64 whereas pandas
  returns float32.
  The underlying reason is the respective Spark operations return DoubleType always.
class_docstr_tok: ["Unit","tests","for","arithmetic","operations","of","numeric","data","types",".","A","few","test","cases","are","disabled","because","pandas-on-Spark","returns","float64","whereas","pandas","returns","float32",".","The","underlying","reason","is","the","respective","Spark","operations","return","DoubleType","always","."]
code: def test_le(self):
      pdf, psdf = self.pdf, self.psdf
      for col in self.numeric_df_cols:
          pser, psser = pdf[col], psdf[col]
          if isinstance(psser.spark.data_type, DecimalType):
              self.assertRaisesRegex(
                  TypeError,
                  "<= can not be applied to",
                  lambda: psser <= psser,
              )
          else:
              self.assert_eq(pser <= pser, psser <= psser)
  
imports: import unittest
  import pandas
  import numpy
  from pyspark import pandas
  from pyspark.pandas.config import option_context
  from pyspark.testing.pandasutils import PandasOnSparkTestCase
  from pyspark.pandas.tests.data_type_ops.testing_utils import OpsTestBase
  from pyspark.pandas.typedef.typehints import extension_dtypes_available, extension_float_dtypes_available, extension_object_dtypes_available
  from pyspark.sql.types import DecimalType, IntegralType
label_desc: Use image node_id 15 for calling the NumOpsTestsMixin obj's underlying member method code with example usage: obj.test_le() without return types