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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 _read_interleaved_numpy(self, f, data_objects):
 """Read interleaved data where all channels have a numpy type"""


        log.debug("Reading interleaved data all at once")
        # Read all data into 1 byte unsigned ints first
        all_channel_bytes = data_objects[0].raw_data_width
        if all_channel_bytes == 0:
            all_channel_bytes = sum((o.data_type.size for o in data_objects))
        log.debug("all_channel_bytes: %d", all_channel_bytes)
        number_bytes = int(all_channel_bytes * data_objects[0].number_values)
        combined_data = fromfile(f, dtype=np.uint8, count=number_bytes)
        # Reshape, so that one row is all bytes for all objects
        combined_data = combined_data.reshape(-1, all_channel_bytes)
        # Now set arrays for each channel
        data_pos = 0
        for (i, obj) in enumerate(data_objects):
            byte_columns = tuple(
                range(data_pos, obj.data_type.size + data_pos))
            log.debug("Byte columns for channel %d: %s", i, byte_columns)
            # Select columns for this channel, so that number of values will
            # be number of bytes per point * number of data points.
            # Then use ravel to flatten the results into a vector.
            object_data = combined_data[:, byte_columns].ravel()
            # Now set correct data type, so that the array length should
            # be correct
            object_data.dtype = (
                np.dtype(obj.data_type.nptype).newbyteorder(self.endianness))
            obj.tdms_object._update_data(object_data)
            data_pos += obj.data_type.size