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: Returns the covariance matrix for given time values.
  
  Parameters
  ----------
  time : array_like
      The time points for the observations.  If len(time) = p,
      a pxp covariance matrix is returned.
  sc : array_like
      The scaling parameters for the observations.
  sm : array_like
      The smoothness parameters for the observation.  See class
      docstring for details.
code: def get_cov(self, time, sc, sm):
      """
      Returns the covariance matrix for given time values.
  
      Parameters
      ----------
      time : array_like
          The time points for the observations.  If len(time) = p,
          a pxp covariance matrix is returned.
      sc : array_like
          The scaling parameters for the observations.
      sm : array_like
          The smoothness parameters for the observation.  See class
          docstring for details.
      """
      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 1 for calling the ProcessCovariance obj's underlying member method code with example usage: obj.get_cov(time, sc, sm) without return types