parameters: (self, points_to_sample, num_derivatives=-1)
return_statement: return cpp_utils.uncppify(grad_mu, (num_derivatives, (1 + self._num_derivatives), self.dim))
docstring: r"""Compute the gradient of the mean of this GP at each of point of ``Xs`` (``points_to_sample``) wrt ``Xs``.
  
          ``points_to_sample`` may not contain duplicate points. Violating this results in singular covariance matrices.
  
          Note that ``grad_mu`` is nominally sized: ``grad_mu[num_to_sample][num_to_sample][dim]``. This is
          the the d-th component of the derivative evaluated at the i-th input wrt the j-th input.
          However, for ``0 <= i,j < num_to_sample``, ``i != j``, ``grad_mu[j][i][d] = 0``.
          (See references or implementation for further details.)
          Thus, ``grad_mu`` is stored in a reduced form which only tracks the nonzero entries.
  
          .. Note:: Comments are copied from
            :mod:`moe.optimal_learning.python.interfaces.gaussian_process_interface.GaussianProcessInterface.compute_grad_mean_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 num_derivatives: return derivatives wrt points_to_sample[0:num_derivatives]; large or negative values are clamped
          :type num_derivatives: int
          :return: grad_mu: gradient of the mean of the GP. ``grad_mu[i][d]`` is actually the gradient
            of ``\mu_i`` wrt ``x_{i,d}``, the d-th dim of the i-th entry of ``points_to_sample``.
          :rtype: array of float64 with shape (num_to_sample, dim)
docstring_summary: r"""Compute the gradient of the mean of this GP at each of point of ``Xs`` (``points_to_sample``) wrt ``Xs``.
docstring_tokens: r Compute the gradient of the mean of this GP at each of point of Xs ( points_to_sample ) wrt Xs .
function: def compute_grad_mean_of_points(self, points_to_sample, num_derivatives=-1):
          r"""Compute the gradient of the mean of this GP at each of point of ``Xs`` (``points_to_sample``) wrt ``Xs``.
  
          ``points_to_sample`` may not contain duplicate points. Violating this results in singular covariance matrices.
  
          Note that ``grad_mu`` is nominally sized: ``grad_mu[num_to_sample][num_to_sample][dim]``. This is
          the the d-th component of the derivative evaluated at the i-th input wrt the j-th input.
          However, for ``0 <= i,j < num_to_sample``, ``i != j``, ``grad_mu[j][i][d] = 0``.
          (See references or implementation for further details.)
          Thus, ``grad_mu`` is stored in a reduced form which only tracks the nonzero entries.
  
          .. Note:: Comments are copied from
            :mod:`moe.optimal_learning.python.interfaces.gaussian_process_interface.GaussianProcessInterface.compute_grad_mean_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 num_derivatives: return derivatives wrt points_to_sample[0:num_derivatives]; large or negative values are clamped
          :type num_derivatives: int
          :return: grad_mu: gradient of the mean of the GP. ``grad_mu[i][d]`` is actually the gradient
            of ``\mu_i`` wrt ``x_{i,d}``, the d-th dim of the i-th entry of ``points_to_sample``.
          :rtype: array of float64 with shape (num_to_sample, dim)
  
          """
          num_derivatives = self._clamp_num_derivatives(points_to_sample.shape[0], num_derivatives)
          grad_mu = self._gaussian_process.compute_grad_mean_of_points(
              cpp_utils.cppify(points_to_sample[:num_derivatives, ...]),
              num_derivatives,
          )
          return cpp_utils.uncppify(grad_mu, (num_derivatives, (1 + self._num_derivatives), self.dim))
function_tokens: def compute_grad_mean_of_points ( self , points_to_sample , num_derivatives = - 1 ) : num_derivatives = self . _clamp_num_derivatives ( points_to_sample . shape [ 0 ] , num_derivatives ) grad_mu = self . _gaussian_process . compute_grad_mean_of_points ( cpp_utils . cppify ( points_to_sample [ : num_derivatives , ... ] ) , num_derivatives , ) return cpp_utils . uncppify ( grad_mu , ( num_derivatives , ( 1 + self . _num_derivatives ) , self . dim ) )