class_docstr: A neural reward network based bandit agent.
  
  This agent receives a neural network that it trains to predict rewards. The
  action is chosen greedily with respect to the prediction.
class_docstr_tok: ["A","neural","reward","network","based","bandit","agent",".","This","agent","receives","a","neural","network","that","it","trains","to","predict","rewards",".","The","action","is","chosen","greedily","with","respect","to","the","prediction","."]
code: def num_samples(self):
      return self._num_samples_list
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Iterable, Optional, Sequence, Text, Tuple
  from absl import logging
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import greedy_reward_prediction_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import utils
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
label_desc: Use image node_id 2 for calling the GreedyRewardPredictionAgent obj's underlying member method code with example usage: obj.num_samples() and returns: self