class_docstr: Results class for Gaussian process regression models.
docstr: Returns a fitted covariance matrix.
  
  Parameters
  ----------
  time : array_like
      The time points at which the fitted covariance
      matrix is calculated.
  scale : array_like
      The data used to determine the scale parameter,
      must have len(time) rows.
  smooth : array_like
      The data used to determine the smoothness parameter,
      must have len(time) rows.
  
  Returns
  -------
  A covariance matrix.
  
  Notes
  -----
  If the model was fit using formulas, `scale` and `smooth` should
  be Dataframes, containing all variables that were present in the
  respective scaling and smoothing formulas used to fit the model.
  Otherwise, `scale` and `smooth` should be data arrays whose
  columns align with the fitted scaling and smoothing parameters.
code: def covariance(self, time, scale, smooth):
      """
      Returns a fitted covariance matrix.
  
      Parameters
      ----------
      time : array_like
          The time points at which the fitted covariance
          matrix is calculated.
      scale : array_like
          The data used to determine the scale parameter,
          must have len(time) rows.
      smooth : array_like
          The data used to determine the smoothness parameter,
          must have len(time) rows.
  
      Returns
      -------
      A covariance matrix.
  
      Notes
      -----
      If the model was fit using formulas, `scale` and `smooth` should
      be Dataframes, containing all variables that were present in the
      respective scaling and smoothing formulas used to fit the model.
      Otherwise, `scale` and `smooth` should be data arrays whose
      columns align with the fitted scaling and smoothing parameters.
      """
  
      return self.model.covariance(
          time, self.scale_params, self.smooth_params, scale, smooth
      )
  
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 3 for calling the ProcessMLEResults obj's underlying member method code with example usage: obj.covariance(time, scale, smooth) and returns: self