code: def make_fake_dist(means, stds, dims=10):
      """
      Create some fake discrete distributions
      Args:
          means: List of means
          stds: List of standard deviations
          dims: Dimensions of the distributions
  
      Returns:
          List of distributions
      """
      n_trials = 100
      distributions = []
      for m, std in zip(means, stds):
          dist = np.zeros([dims])
          for i in range(n_trials):
              dist[
                  np.round(
                      np.clip(np.random.normal(m, std, 1), 0, dims - 1)
                  )
                  .astype(int)
                  .item()
              ] += 1
          distributions.append(dist / n_trials)
      return np.array(distributions)
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: make_fake_dist(means, stds, dims) and returns: np