code: def gaussian_likelihood(x, mu, log_std):
      pre_sum = -0.5 * (
          ((x - mu) / (tf.exp(log_std) + EPS)) ** 2
          + 2 * log_std
          + np.log(2 * np.pi)
      )
      return tf.reduce_sum(pre_sum, axis=1)
  
imports: import numpy
  import tensorflow
  import scipy.signal
  from gym.spaces import Box, Discrete
label_desc: Use image node_id 11 for calling a global function with example usage: gaussian_likelihood(x, mu, log_std) and returns: tf