code: def _fit_gaussian(samples, mean_size):
      """Fit a Gaussian to represent the mean of `mean_size` of the `samples`."""
      loc = np.mean(samples)
      scale_one = np.sqrt(np.mean((samples - loc) ** 2))
      scale = scale_one / np.sqrt(mean_size)
      rho = np.mean(np.abs(samples - loc) ** 3)
      # Upper bound from Wikipedia
      # https://en.wikipedia.org/wiki/Berry%E2%80%93Esseen_theorem
      berry_esseen_c = 0.4748
      # From the Berry-Esseen theorem
      k_s_limit = (
          berry_esseen_c * rho / (np.sqrt(mean_size) * (scale_one**3))
      )
      return loc, scale, k_s_limit
  
imports: import collections
  import numpy
  import scipy.optimize
  import scipy.stats
label_desc: Use image node_id 5 for calling a global function with example usage: _fit_gaussian(samples, mean_size) and returns: loc,  scale,  k_s_limit