code: def normal_log_prob(self, val):
      loc = self.parameters["loc"]
      scale = self.parameters["scale"]
      prec = tf.reciprocal(tf.square(scale))
      result = prec * (
          -0.5 * tf.square(val) - 0.5 * tf.square(loc) + val * loc
      )
      result -= tf.log(scale) + 0.5 * tf.cast(
          tf.log(2 * np.pi), dtype=result.dtype
      )
      return result
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from edward.models.random_variables import *
label_desc: Use image node_id 14 for calling a global function with example usage: normal_log_prob(val) and returns: result