code: 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 (1.0 / np.sqrt(2 * np.pi)) * np.exp(
          -((Xi - x) ** 2) / (h**2 * 2.0)
      )
  
file_path: statsmodels/statsmodels/nonparametric/kernels.py
imports: import numpy
  from scipy.special import erf
label_desc: Use image node_id 3 for calling a global function with example usage: gaussian(h, Xi, x) and returns: unknown