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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):
 """ Execute method that processes EOPatch and returns EOPatch """

        # pylint: disable=too-many-locals
        feature_type, feature_name, new_feature_name = next(self.feature(eopatch))

        # Make a copy not to change original numpy array
        feature_data = eopatch[feature_type][feature_name].copy()
        time_num, height, width, band_num = feature_data.shape
        if time_num <= 1:
            raise ValueError('Feature {} has time dimension of size {}, required at least size '
                             '2'.format((feature_type, feature_name), time_num))

        # Apply a mask on data
        if self.mask_feature is not None:
            for mask_type, mask_name in self.mask_feature(eopatch):
                negated_mask = ~eopatch[mask_type][mask_name].astype(np.bool)
                feature_data = self._mask_feature_data(feature_data, negated_mask, mask_type)

        # Flatten array
        feature_data = np.reshape(feature_data, (time_num, height * width * band_num))

        # If resampling create new EOPatch
        new_eopatch = EOPatch() if self.resample_range else eopatch

        # Resample times
        times = eopatch.time_series(scale_time=self.scale_time)
        new_eopatch.timestamp = self.get_resampled_timestamp(eopatch.timestamp)
        total_diff = int((new_eopatch.timestamp[0].date() - eopatch.timestamp[0].date()).total_seconds())
        resampled_times = new_eopatch.time_series(scale_time=self.scale_time) + total_diff // self.scale_time

        # Add BBox to eopatch if it was created anew
        if new_eopatch.bbox is None:
            new_eopatch.bbox = eopatch.bbox

        # Replace duplicate acquisitions which have same values on the chosen time scale with their average
        feature_data, times = self._get_unique_times(feature_data, times)

        # Interpolate
        feature_data = self.interpolate_data(feature_data, times, resampled_times)

        # Normalize
        if self.result_interval:
            min_val, max_val = self.result_interval
            valid_mask = ~np.isnan(feature_data)
            feature_data[valid_mask] = np.maximum(np.minimum(feature_data[valid_mask], max_val), min_val)

        # Replace unknown value
        if not np.isnan(self.unknown_value):
            feature_data[np.isnan(feature_data)] = self.unknown_value

        # Reshape back
        new_eopatch[feature_type][new_feature_name] = np.reshape(feature_data,
                                                                 (feature_data.shape[0], height, width, band_num))

        # append features from old patch
        new_eopatch = self._copy_old_features(new_eopatch, eopatch, self.copy_features)

        return new_eopatch