docs: def gaussian(h, Xi, x):
      """
      Gaussian Kernel for continuous variables
      Parameters
      ----------
      h : 1-D ndarray, shape (K,)
          The bandwidths used to estimate the value of the kernel function.
      Xi : 1-D ndarray, shape (K,)
          The value of the training set.
      x : 1-D ndarray, shape (K,)
          The value at which the kernel density is being estimated.
  
      Returns
      -------
      kernel_value : ndarray, shape (nobs, K)
          The value of the kernel function at each training point for each var.
  
      """
      return (1. / np.sqrt(2 * np.pi)) * np.exp(-(Xi - x)**2 / (h**2 * 2.))