parameters: (self, point_to_sample, noise_variance=0.0)
docstring: r"""Sample a function value from a Gaussian Process prior, provided a point at which to sample.
  
          Uses the formula ``function_value = gpp_mean + sqrt(gpp_variance) * w1 + sqrt(noise_variance) * w2``, where ``w1, w2``
          are draws from N(0,1).
  
          Implementers are responsible for providing a N(0,1) source.
  
          .. NOTE::
               Set noise_variance to 0 if you want "accurate" draws from the GP.
               BUT if the drawn (point, value) pair is meant to be added back into the GP (e.g., for testing), then this point
               MUST be drawn with noise_variance equal to the noise associated with "point" as a member of "points_sampled"
  
          .. Note:: Comments are copied from GaussianProcess in gpp_math.hpp and duplicated in
            :meth:`moe.optimal_learning.python.cpp_wrappers.gaussian_process.GaussianProcess.sample_point_from_gp`.
  
          :param point_to_sample: point (in dim dimensions) at which to sample from this GP
          :type points_to_sample: array of float64 with shape (dim)
          :param noise_variance: amount of noise to associate with the sample
          :type noise_variance: float64 >= 0.0
          :return: sample_value: function value drawn from this GP
          :rtype: float64
docstring_summary: r"""Sample a function value from a Gaussian Process prior, provided a point at which to sample.
docstring_tokens: r Sample a function value from a Gaussian Process prior provided a point at which to sample .
function: def sample_point_from_gp(self, point_to_sample, noise_variance=0.0):
          r"""Sample a function value from a Gaussian Process prior, provided a point at which to sample.
  
          Uses the formula ``function_value = gpp_mean + sqrt(gpp_variance) * w1 + sqrt(noise_variance) * w2``, where ``w1, w2``
          are draws from N(0,1).
  
          Implementers are responsible for providing a N(0,1) source.
  
          .. NOTE::
               Set noise_variance to 0 if you want "accurate" draws from the GP.
               BUT if the drawn (point, value) pair is meant to be added back into the GP (e.g., for testing), then this point
               MUST be drawn with noise_variance equal to the noise associated with "point" as a member of "points_sampled"
  
          .. Note:: Comments are copied from GaussianProcess in gpp_math.hpp and duplicated in
            :meth:`moe.optimal_learning.python.cpp_wrappers.gaussian_process.GaussianProcess.sample_point_from_gp`.
  
          :param point_to_sample: point (in dim dimensions) at which to sample from this GP
          :type points_to_sample: array of float64 with shape (dim)
          :param noise_variance: amount of noise to associate with the sample
          :type noise_variance: float64 >= 0.0
          :return: sample_value: function value drawn from this GP
          :rtype: float64
  
          """
          pass
function_tokens: def sample_point_from_gp ( self , point_to_sample , noise_variance = 0.0 ) : pass