class_docstr: A categorical distribution which supports masks per step.
  
  Masked values are replaced with -inf inside the logits. This means the values
  will never be sampled.
  
  When computing the log probability of a set of actions, each action is
  assigned a probability under each sample. _log_prob is modified to only return
  the probability of a sample under the distribution for the same timestep.
  
  TODO(ddohan): Integrate entropy calculation from cl/207017752
code: def _entropy(self):
      entropy = tf.nn.log_softmax(self.logits) * self.probs_parameter()
      # Replace the (potentially -inf) values with 0s before summing.
      entropy = tf.compat.v1.where(
          self._mask, entropy, tf.zeros_like(entropy)
      )
      return -tf.reduce_sum(input_tensor=entropy, axis=-1)
  
file_path: agents/tf_agents/distributions/masked.py
imports: import tensorflow
  import tensorflow_probability
label_desc: Use image node_id 2 for calling the MaskedCategorical obj's underlying member method code with example usage: obj._entropy() and returns: unknown