class_docstr: Creates a categorical Q-network.
  
  It can be used to take an input of batched observations and outputs
  ([batch_size, num_actions, num_atoms], network's state).
  
  The first element of the output is a batch of logits based on the distribution
  called C51 from Bellemare et al., 2017 (https://arxiv.org/abs/1707.06887). The
  logits are used to compute approximate probability distributions for Q-values
  for each potential action, by computing the probabilities at the 51 points
  (called atoms) in np.linspace(-10.0, 10.0, 51).
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code: def num_atoms(self):
      return self._num_atoms
  
file_path: agents/tf_agents/networks/categorical_q_network.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  from tf_agents.networks import network
  from tf_agents.networks import q_network
  from tf_agents.specs import tensor_spec
label_desc: Use image node_id 2 for calling the CategoricalQNetwork obj's underlying member method code with example usage: obj.num_atoms() and returns: self