code: def gaussian_likelihood(x, mu, log_std):
      pre_sum = -0.5 * (
          ((x - mu) / (torch.exp(log_std) + EPS)) ** 2
          + 2 * log_std
          + np.log(2 * np.pi)
      )
      return pre_sum.sum(axis=-1)
  
file_path: spinningup/spinup/exercises/pytorch/problem_set_1_solutions/exercise1_2_soln.py
imports: import torch
  import torch.nn
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: gaussian_likelihood(x, mu, log_std) and returns: pre_sum