class_docstr: Generic N-dimensial kernel
  
  Parameters
  ----------
  n : int
      The number of series for kernel estimates
  kernels : list
      kernels
  
  Can be constructed from either
  a) a list of n kernels which will be treated as
  indepent marginals on a gaussian copula (specified by H)
  or b) a single univariate kernel which will be applied radially to the
  mahalanobis distance defined by H.
  
  In the case of the Gaussian these are both equivalent, and the second constructiong
  is prefered.
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docstr: Getter for kernel bandwidth, H
code: def getH(self):
      """Getter for kernel bandwidth, H"""
      return self._H
  
imports: from statsmodels.compat.python import lzip, lfilter
  import numpy
  import scipy.integrate
  from scipy.special import factorial
  from numpy import exp, multiply, square, divide, subtract, inf
label_desc: Use image node_id 2 for calling the NdKernel obj's underlying member method code with example usage: obj.getH() and returns: self