code: def interpolate(self, factor, minKerning, maxKerning, round=True, suppressError=True):
          
          factor = normalizers.normalizeInterpolationFactor(factor)
          if not isinstance(minKerning, BaseKerning):
              raise TypeError(("Interpolation to an instance of %r can not be "
                               "performed from an instance of %r.") % (
                  self.__class__.__name__, minKerning.__class__.__name__))
          if not isinstance(maxKerning, BaseKerning):
              raise TypeError(("Interpolation to an instance of %r can not be "
                               "performed from an instance of %r.") % (
                  self.__class__.__name__, maxKerning.__class__.__name__))
          round = normalizers.normalizeBoolean(round)
          suppressError = normalizers.normalizeBoolean(suppressError)
          self._interpolate(factor, minKerning, maxKerning,
                            round=round, suppressError=suppressError)
docs: Interpolates all pairs between two :class:`BaseKerning` objects:
  
          **minKerning** and **maxKerning**. The interpolation occurs on a
          0 to 1.0 range where **minKerning** is located at 0 and
          **maxKerning** is located at 1.0. The kerning data is replaced by
          the interpolated kerning.
  
          * **factor** is the interpolation value. It may be less than 0
            and greater than 1.0. It may be an :ref:`type-int-float`,
            ``tuple`` or ``list``. If it is a ``tuple`` or ``list``,
            the first number indicates the x factor and the second number
            indicates the y factor.
          * **round** is a ``bool`` indicating if the result should be rounded to
            ``int``\s. The default behavior is to round interpolated kerning.
          * **suppressError** is a ``bool`` indicating if incompatible data should
            be ignored or if an error should be raised when such incompatibilities
            are found. The default behavior is to ignore incompatible data.
  
              >>> myKerning.interpolate(kerningOne, kerningTwo)