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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 execute(self, eopatch):
 """ Computation of NDVI slope using finite central differences This implementation loops through every spatial location, considers the valid NDVI values and approximates their first order derivative using central differences. The argument of min and max is added to the eopatch. The NDVI slope at date t is comuted as $(NDVI_{t+1}-NDVI_{t-1})/(date_{t+1}-date_{t-1})$. :param eopatch: Input eopatch :return: eopatch with NDVI slope argmin/argmax features """

        # pylint: disable=invalid-name
        if self.mask_data:
            valid_data_mask = eopatch.mask['VALID_DATA']
        else:
            valid_data_mask = eopatch.mask['IS_DATA']

        ndvi = np.ma.array(eopatch.data[self.data_feature],
                           dtype=np.float32,
                           mask=~valid_data_mask.astype(np.bool))

        all_dates = np.asarray([x.toordinal() for x in eopatch.timestamp])

        if ndvi.ndim == 4:
            h, w = ndvi.shape[1: 3]
        else:
            raise ValueError('{} feature has incorrect number of dimensions'.format(self.data_feature))

        argmax_ndvi_slope, argmin_ndvi_slope = np.zeros((h, w, 1), dtype=np.uint8), np.zeros((h, w, 1), dtype=np.uint8)

        for ih, iw in it.product(range(h), range(w)):

            ndvi_curve = ndvi[:, ih, iw, :]
            valid_idx = np.where(~ndvi.mask[:, ih, iw])[0]

            ndvi_curve = ndvi_curve[valid_idx]
            valid_dates = all_dates[valid_idx]

            ndvi_slope = np.convolve(ndvi_curve.squeeze(), [1, 0, -1], 'valid') / np.convolve(valid_dates, [1, 0, -1],
                                                                                              'valid')

            # +1 to compensate for the 'valid' convolution which eliminates first and last
            argmax_ndvi_slope[ih, iw] = valid_idx[np.argmax(ndvi_slope) + 1]
            argmin_ndvi_slope[ih, iw] = valid_idx[np.argmin(ndvi_slope) + 1]

            del ndvi_curve, valid_idx, valid_dates, ndvi_slope

        eopatch.data_timeless[self.argmax_feature] = argmax_ndvi_slope
        eopatch.data_timeless[self.argmin_feature] = argmin_ndvi_slope

        return eopatch