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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, overwrite=True):
 """ Perform column-wise combine with another DataFrame. Combines a DataFrame with `other` DataFrame using `func` to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two. Parameters other : DataFrame The DataFrame to merge column-wise. func : function Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns. fill_value : scalar value, default None The value to fill NaNs with prior to passing any column to the merge func. overwrite : bool, default True If True, columns in `self` that do not exist in `other` will be overwritten with NaNs. Returns ------- DataFrame Combination of the provided DataFrames. See Also -------- DataFrame.combine_first : Combine two DataFrame objects and default to non-null values in frame calling the method. Examples -------- Combine using a simple function that chooses the smaller column. A B 0 0 3 1 0 3 Example using a true element-wise combine function. A B 0 1 2 1 0 3 Using `fill_value` fills Nones prior to passing the column to the merge function. A B 0 0 -5.0 1 0 4.0 However, if the same element in both dataframes is None, that None is preserved A B 0 0 -5.0 1 0 3.0 Example that demonstrates the use of `overwrite` and behavior when the axis differ between the dataframes. A B C 0 NaN NaN NaN 1 NaN 3.0 -10.0 2 NaN 3.0 1.0 A B C 0 0.0 NaN NaN 1 0.0 3.0 -10.0 2 NaN 3.0 1.0 Demonstrating the preference of the passed in dataframe. A B C 0 0.0 NaN NaN 1 0.0 3.0 NaN 2 NaN 3.0 NaN A B C 0 0.0 NaN NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0 """

        other_idxlen = len(other.index)  # save for compare

        this, other = self.align(other, copy=False)
        new_index = this.index

        if other.empty and len(new_index) == len(self.index):
            return self.copy()

        if self.empty and len(other) == other_idxlen:
            return other.copy()

        # sorts if possible
        new_columns = this.columns.union(other.columns)
        do_fill = fill_value is not None
        result = {}
        for col in new_columns:
            series = this[col]
            otherSeries = other[col]

            this_dtype = series.dtype
            other_dtype = otherSeries.dtype

            this_mask = isna(series)
            other_mask = isna(otherSeries)

            # don't overwrite columns unecessarily
            # DO propagate if this column is not in the intersection
            if not overwrite and other_mask.all():
                result[col] = this[col].copy()
                continue

            if do_fill:
                series = series.copy()
                otherSeries = otherSeries.copy()
                series[this_mask] = fill_value
                otherSeries[other_mask] = fill_value

            if col not in self.columns:
                # If self DataFrame does not have col in other DataFrame,
                # try to promote series, which is all NaN, as other_dtype.
                new_dtype = other_dtype
                try:
                    series = series.astype(new_dtype, copy=False)
                except ValueError:
                    # e.g. new_dtype is integer types
                    pass
            else:
                # if we have different dtypes, possibly promote
                new_dtype = find_common_type([this_dtype, other_dtype])
                if not is_dtype_equal(this_dtype, new_dtype):
                    series = series.astype(new_dtype)
                if not is_dtype_equal(other_dtype, new_dtype):
                    otherSeries = otherSeries.astype(new_dtype)

            arr = func(series, otherSeries)
            arr = maybe_downcast_to_dtype(arr, this_dtype)

            result[col] = arr

        # convert_objects just in case
        return self._constructor(result, index=new_index,
                                 columns=new_columns)