parameters: (self, points_to_sample, num_derivatives)
docstring: r"""Compute the gradient of the cholesky factorization of the variance (matrix) of this GP at each point of ``Xs`` (``points_to_sample``) wrt ``Xs``.
  
          ``points_to_sample`` may not contain duplicate points. Violating this results in singular covariance matrices.
  
          This function accounts for the effect on the gradient resulting from
          cholesky-factoring the variance matrix.  See Smith 1995 for algorithm details.
  
          Note that ``grad_chol`` is nominally sized:
          ``grad_chol[num_to_sample][num_to_sample][num_to_sample][dim]``.
          Let this be indexed ``grad_chol[k][j][i][d]``, which is read the derivative of ``var[j][i]``
          with respect to ``x_{k,d}`` (x = ``points_to_sample``)
  
          .. Note:: Comments are copied from GaussianProcess in gpp_math.hpp and duplicated in
            :meth:`moe.optimal_learning.python.cpp_wrappers.gaussian_process.GaussianProcess.compute_grad_cholesky_variance_of_points`.
  
          :param points_to_sample: num_to_sample points (in dim dimensions) being sampled from the GP
          :type points_to_sample: array of float64 with shape (num_to_sample, dim)
          :param var_of_grad: index of ``points_to_sample`` to be differentiated against
          :type var_of_grad: integer in {0, .. ``num_to_sample``-1}
          :return: grad_chol: gradient of the cholesky factorization of the variance matrix of this GP.
            ``grad_chol[k][j][i][d]`` is actually the gradients of ``var_{j,i}`` with
            respect to ``x_{k,d}``, the d-th dimension of the k-th entry of ``points_to_sample``
          :rtype: array of float64 with shape (num_derivatives, num_to_sample, num_to_sample, dim)
docstring_summary: r"""Compute the gradient of the cholesky factorization of the variance (matrix) of this GP at each point of ``Xs`` (``points_to_sample``) wrt ``Xs``.
docstring_tokens: r Compute the gradient of the cholesky factorization of the variance ( matrix ) of this GP at each point of Xs ( points_to_sample ) wrt Xs .
function: def compute_grad_cholesky_variance_of_points(self, points_to_sample, num_derivatives):
          r"""Compute the gradient of the cholesky factorization of the variance (matrix) of this GP at each point of ``Xs`` (``points_to_sample``) wrt ``Xs``.
  
          ``points_to_sample`` may not contain duplicate points. Violating this results in singular covariance matrices.
  
          This function accounts for the effect on the gradient resulting from
          cholesky-factoring the variance matrix.  See Smith 1995 for algorithm details.
  
          Note that ``grad_chol`` is nominally sized:
          ``grad_chol[num_to_sample][num_to_sample][num_to_sample][dim]``.
          Let this be indexed ``grad_chol[k][j][i][d]``, which is read the derivative of ``var[j][i]``
          with respect to ``x_{k,d}`` (x = ``points_to_sample``)
  
          .. Note:: Comments are copied from GaussianProcess in gpp_math.hpp and duplicated in
            :meth:`moe.optimal_learning.python.cpp_wrappers.gaussian_process.GaussianProcess.compute_grad_cholesky_variance_of_points`.
  
          :param points_to_sample: num_to_sample points (in dim dimensions) being sampled from the GP
          :type points_to_sample: array of float64 with shape (num_to_sample, dim)
          :param var_of_grad: index of ``points_to_sample`` to be differentiated against
          :type var_of_grad: integer in {0, .. ``num_to_sample``-1}
          :return: grad_chol: gradient of the cholesky factorization of the variance matrix of this GP.
            ``grad_chol[k][j][i][d]`` is actually the gradients of ``var_{j,i}`` with
            respect to ``x_{k,d}``, the d-th dimension of the k-th entry of ``points_to_sample``
          :rtype: array of float64 with shape (num_derivatives, num_to_sample, num_to_sample, dim)
  
          """
          pass
function_tokens: def compute_grad_cholesky_variance_of_points ( self , points_to_sample , num_derivatives ) : pass