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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 time_track(self, absolute_time=False, accuracy='ns'):
 """Return an array of time or the independent variable for this channel This depends on the object having the wf_increment and wf_start_offset properties defined. Note that wf_start_offset is usually zero for time-series data. If you have time-series data channels with different start times, you should use the absolute time or calculate the time offsets using the wf_start_time property. For larger timespans, the accuracy setting should be set lower. The default setting is 'ns', which has a timespan of [1678 AD, 2262 AD]. For the exact ranges, refer to http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html section "Datetime Units". :param absolute_time: Whether the returned time values are absolute times rather than relative to the start time. If true, the wf_start_time property must be set. :param accuracy: The accuracy of the returned datetime64 array. :rtype: NumPy array. :raises: KeyError if required properties aren't found """


        try:
            increment = self.property('wf_increment')
            offset = self.property('wf_start_offset')
        except KeyError:
            raise KeyError("Object does not have time properties available.")

        periods = len(self._data)

        relative_time = np.linspace(
            offset,
            offset + (periods - 1) * increment,
            periods)

        if not absolute_time:
            return relative_time

        try:
            start_time = self.property('wf_start_time')
        except KeyError:
            raise KeyError(
                "Object does not have start time property available.")

        try:
            unit_correction = {
                's': 1e0,
                'ms': 1e3,
                'us': 1e6,
                'ns': 1e9,
            }[accuracy]
        except KeyError:
            raise KeyError("Invalid accuracy: {0}".format(accuracy))

        # Because numpy only knows ints as its date datatype,
        # convert to accuracy.
        time_type = "timedelta64[{0}]".format(accuracy)
        return (np.datetime64(start_time) +
                (relative_time * unit_correction).astype(time_type))