code: def eq(self, other):
          
          # We treat this as an error because missing_values have NaN semantics,
          # which means this would return an array of all False, which is almost
          # certainly not what the user wants.
          if other == self.missing_value:
              raise ValueError(
                  "Comparison against self.missing_value ({value!r}) in"
                  " {typename}.eq().\n"
                  "Missing values have NaN semantics, so the "
                  "requested comparison would always produce False.\n"
                  "Use the isnull() method to check for missing values.".format(
                      value=other,
                      typename=(type(self).__name__),
                  )
              )
  
          if isinstance(other, Number) != (self.dtype == int64_dtype):
              raise InvalidClassifierComparison(self, other)
  
          if isinstance(other, Number):
              return NumExprFilter.create(
                  "x_0 == {other}".format(other=int(other)),
                  binds=(self,),
              )
          else:
              return ArrayPredicate(
                  term=self,
                  op=operator.eq,
                  opargs=(other,),
              )
docs: Construct a Filter returning True for asset/date pairs where the output
          of ``self`` matches ``other``.