parameters: (self)
docstring: r"""Compute the gradient (wrt hyperparameters) of this log likelihood measure of model fit.
  
          :return: grad_log_likelihood: i-th entry is ``\pderiv{LL(y | X, \theta)}{\theta_i}``
          :rtype: array of float64 with shape (num_hyperparameters)
docstring_summary: r"""Compute the gradient (wrt hyperparameters) of this log likelihood measure of model fit.
docstring_tokens: r Compute the gradient ( wrt hyperparameters ) of this log likelihood measure of model fit .
function: def compute_grad_log_likelihood(self):
          r"""Compute the gradient (wrt hyperparameters) of this log likelihood measure of model fit.
  
          :return: grad_log_likelihood: i-th entry is ``\pderiv{LL(y | X, \theta)}{\theta_i}``
          :rtype: array of float64 with shape (num_hyperparameters)
  
          """
          pass
function_tokens: def compute_grad_log_likelihood ( self ) : pass