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 mask(self):
      return self._mask
  
file_path: agents/tf_agents/distributions/masked.py
imports: import tensorflow
  import tensorflow_probability
label_desc: Use image node_id 4 for calling the MaskedCategorical obj's underlying member method code with example usage: obj.mask() and returns: self