code: def dot(self, y, t=None, A=None, U=None, V=None, kernel=None,
              check_sorted=True):
          
          if kernel is None:
              kernel = self.kernel
  
          if t is not None:
              t = np.atleast_1d(t)
              if check_sorted and np.any(np.diff(t) < 0.0):
                  raise ValueError("the input coordinates must be sorted")
              if check_sorted and len(t.shape) > 1:
                  raise ValueError("dimension mismatch")
  
              A = np.empty(0) if A is None else A
              U = np.empty((0, 0)) if U is None else U
              V = np.empty((0, 0)) if V is None else V
          else:
              if not self.computed:
                  raise RuntimeError("you must call 'compute' first")
              t = self._t
              A = self._A
              U = self._U
              V = self._V
  
          (alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
           beta_complex_real, beta_complex_imag) = kernel.coefficients
  
          return self.solver.dot(
              kernel.jitter,
              alpha_real, beta_real,
              alpha_complex_real, alpha_complex_imag,
              beta_complex_real, beta_complex_imag,
              A, U, V, t, np.ascontiguousarray(y, dtype=float)
          )
docs: Dot the covariance matrix into a vector or matrix
  
          Compute ``K.y`` where ``K`` is the covariance matrix of the GP without
          the white noise or ``yerr`` values on the diagonal.
  
          Args:
              y (array[n] or array[n, nrhs]): The vector or matrix ``y``
                  described above.
              kernel (Optional[terms.Term]): A different kernel can optionally
                  be provided to compute the matrix ``K`` from a different
                  kernel than the ``kernel`` property on this object.
  
          Returns:
              array[n] or array[n, nrhs]: The dot product ``K.y`` as described
              above. This will have the same shape as ``y``.
  
          Raises:
              ValueError: For mismatched dimensions.