code: def beta_log_prob(self, val):
      conc0 = self.parameters["concentration0"]
      conc1 = self.parameters["concentration1"]
      result = (conc1 - 1.0) * tf.log(val)
      result += (conc0 - 1.0) * tf.log(1.0 - val)
      result += (
          -tf.lgamma(conc1)
          - tf.lgamma(conc0)
          + tf.lgamma(conc1 + conc0)
      )
      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 3 for calling a global function with example usage: beta_log_prob(val) and returns: result