class_docstr: A covariance model for a process indexed by a real parameter.
  
  An implementation of this class is based on a positive definite
  correlation function h that maps real numbers to the interval [0,
  1], such as the Gaussian (squared exponential) correlation
  function :math:`\exp(-x^2)`.  It also depends on a positive
  scaling function `s` and a positive smoothness function `u`.
docstr: The Jacobian of the covariance with respect to the parameters.
  
  See get_cov for parameters.
  
  Returns
  -------
  jsc : list-like
      jsc[i] is the derivative of the covariance matrix
      with respect to the i^th scaling parameter.
  jsm : list-like
      jsm[i] is the derivative of the covariance matrix
      with respect to the i^th smoothness parameter.
code: def jac(self, time, sc, sm):
      """
      The Jacobian of the covariance with respect to the parameters.
  
      See get_cov for parameters.
  
      Returns
      -------
      jsc : list-like
          jsc[i] is the derivative of the covariance matrix
          with respect to the i^th scaling parameter.
      jsm : list-like
          jsm[i] is the derivative of the covariance matrix
          with respect to the i^th smoothness parameter.
      """
      raise NotImplementedError
  
imports: import numpy
  import pandas
  import patsy
  import statsmodels.base.model
  from statsmodels.regression.linear_model import OLS
  import collections
  from scipy.optimize import minimize
  from statsmodels.iolib import summary2
  from statsmodels.tools.numdiff import approx_fprime
  import warnings
label_desc: Use image node_id 2 for calling the ProcessCovariance obj's underlying member method code with example usage: obj.jac(time, sc, sm) without return types