code: def parse_samples(self, t):
          
          t = np.atleast_1d(t)
          # Deal with one-dimensional data.
          if len(t.shape) == 1:
              t = np.atleast_2d(t).T
  
          # Double check the dimensions against the kernel.
          if len(t.shape) != 2 or (self.kernel is not None and
                                   t.shape[1] != self.kernel.ndim):
              raise ValueError("Dimension mismatch")
  
          return t
docs: Parse a list of samples to make sure that it has the correct
          dimensions.
  
          :param t: ``(nsamples,)`` or ``(nsamples, ndim)``
              The list of samples. If 1-D, this is assumed to be a list of
              one-dimensional samples otherwise, the size of the second
              dimension is assumed to be the dimension of the input space.
  
          Raises:
              ValueError: If the input dimension doesn't match the dimension of
                  the kernel.