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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def combine(self, other, func, fill_value=None):
 """ Combine the Series with a Series or scalar according to `func`. Combine the Series and `other` using `func` to perform elementwise selection for combined Series. `fill_value` is assumed when value is missing at some index from one of the two objects being combined. Parameters other : Series or scalar The value(s) to be combined with the `Series`. func : function Function that takes two scalars as inputs and returns an element. fill_value : scalar, optional The value to assume when an index is missing from one Series or the other. The default specifies to use the appropriate NaN value for the underlying dtype of the Series. Returns ------- Series The result of combining the Series with the other object. See Also -------- Series.combine_first : Combine Series values, choosing the calling Series' values first. Examples -------- Consider 2 Datasets ``s1`` and ``s2`` containing highest clocked speeds of different birds. falcon 330.0 eagle 160.0 dtype: float64 falcon 345.0 eagle 200.0 duck 30.0 dtype: float64 Now, to combine the two datasets and view the highest speeds of the birds across the two datasets duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because the maximum of a NaN and a float is a NaN. So, in the example, we set ``fill_value=0``, so the maximum value returned will be the value from some dataset. duck 30.0 eagle 200.0 falcon 345.0 dtype: float64 """

        if fill_value is None:
            fill_value = na_value_for_dtype(self.dtype, compat=False)

        if isinstance(other, Series):
            # If other is a Series, result is based on union of Series,
            # so do this element by element
            new_index = self.index.union(other.index)
            new_name = ops.get_op_result_name(self, other)
            new_values = []
            for idx in new_index:
                lv = self.get(idx, fill_value)
                rv = other.get(idx, fill_value)
                with np.errstate(all='ignore'):
                    new_values.append(func(lv, rv))
        else:
            # Assume that other is a scalar, so apply the function for
            # each element in the Series
            new_index = self.index
            with np.errstate(all='ignore'):
                new_values = [func(lv, other) for lv in self._values]
            new_name = self.name

        if is_categorical_dtype(self.values):
            pass
        elif is_extension_array_dtype(self.values):
            # The function can return something of any type, so check
            # if the type is compatible with the calling EA.
            try:
                new_values = self._values._from_sequence(new_values)
            except Exception:
                # https://github.com/pandas-dev/pandas/issues/22850
                # pandas has no control over what 3rd-party ExtensionArrays
                # do in _values_from_sequence. We still want ops to work
                # though, so we catch any regular Exception.
                pass

        return self._constructor(new_values, index=new_index, name=new_name)