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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 download_channel_image(self, channel_name, plate_name,
            well_name, well_pos_y, well_pos_x,
            cycle_index=0, tpoint=0, zplane=0, correct=True, align =False):
        '''Downloads a channel image.

        Parameters
        ----------
        channel_name: str
            name of the channel
        plate_name: str
            name of the plate
        well_name: str
            name of the well
        well_pos_x: int
            zero-based x cooridinate of the acquisition site within the well
        well_pos_y: int
            zero-based y cooridinate of the acquisition site within the well
        cycle_index: str, optional
            zero-based cycle index (default: ``0``)
        tpoint: int, optional
            zero-based time point index (default: ``0``)
        zplane: int, optional
            zero-based z-plane index (default: ``0``)
        correct: bool, optional
            whether image should be corrected for illumination artifacts
            (default: ``True``)

        Note
        ----
        Image gets automatically aligned between cycles.

        Returns
        -------
        numpy.ndarray[numpy.uint16 or numpy.uint8]
            pixel/voxel array and filename

        See also
        --------
        :func:`tmserver.api.file.get_channel_image_file`
        :class:`tmlib.models.file.ChannelImageFile`
        '''
        response = self._download_channel_image(
            channel_name, plate_name, well_name, well_pos_y, well_pos_x,
            cycle_index=cycle_index, tpoint=tpoint, zplane=zplane,
            correct=correct, align = align
        )
        data = np.frombuffer(response.content, np.uint8)
        return cv2.imdecode(data, cv2.IMREAD_UNCHANGED)