class_docstr: A Thompson Sampling agent for non-contextual Bernoulli bandit problems.
  
  In Bernoulli bandit problems, the reward for each arm is a Bernoulli
  distribution with parameter p. The agent assumes that the prior distribution
  is beta distributed, which is conjugate to the bernoulli distribution and
  the posterior distribution of each arm admits simple incremental updates.
  For a reference, see e.g., Chapter 3 in "A Tutorial on Thompson Sampling" by
  Russo et al. (https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf)
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code: def beta(self):
      return self._beta
  
file_path: agents/tf_agents/bandits/agents/bernoulli_thompson_sampling_agent.py
imports: from typing import Optional, Sequence, Text
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.bandits.policies import bernoulli_thompson_sampling_policy
  from tf_agents.policies import utils
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 4 for calling the BernoulliThompsonSamplingAgent obj's underlying member method code with example usage: obj.beta() and returns: self