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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 compute(self, x, yerr=0.0, **kwargs):
 """ Pre-compute the covariance matrix and factorize it for a set of times and uncertainties. :param x: ``(nsamples,)`` or ``(nsamples, ndim)`` The independent coordinates of the data points. :param yerr: (optional) ``(nsamples,)`` or scalar The Gaussian uncertainties on the data points at coordinates ``x``. These values will be added in quadrature to the diagonal of the covariance matrix. """

        # Parse the input coordinates and ensure the right memory layout.
        self._x = self.parse_samples(x)
        self._x = np.ascontiguousarray(self._x, dtype=np.float64)
        try:
            self._yerr2 = float(yerr)**2 * np.ones(len(x))
        except TypeError:
            self._yerr2 = self._check_dimensions(yerr) ** 2
        self._yerr2 = np.ascontiguousarray(self._yerr2, dtype=np.float64)

        # Set up and pre-compute the solver.
        self.solver = self.solver_type(self.kernel, **(self.solver_kwargs))

        # Include the white noise term.
        yerr = np.sqrt(self._yerr2 + np.exp(self._call_white_noise(self._x)))
        self.solver.compute(self._x, yerr, **kwargs)

        self._const = -0.5 * (len(self._x) * np.log(2 * np.pi) +
                              self.solver.log_determinant)
        self.computed = True
        self._alpha = None