code: def tricube(h, Xi, x):
      """
      Tricube 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.
      """
      u = (Xi - x) / h
      u[np.abs(u) > 1] = 0
      return (70.0 / 81) * (1 - np.abs(u) ** 3) ** 3
  
file_path: statsmodels/statsmodels/nonparametric/kernels.py
imports: import numpy
  from scipy.special import erf
label_desc: Use image node_id 4 for calling a global function with example usage: tricube(h, Xi, x) and returns: unknown