class_docstr: Implements finite-armed Bernoulli Bandits.
  
  This environment implements a finite-armed non-contextual Bernoulli Bandit
  environment as a subclass of BanditPyEnvironment. For every arm, the reward
  distribution is 0/1 (Bernoulli) with parameter p set at the initialization.
  For a reference, see e.g., Example 1.1 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 batch_size(self) -> types.Int:
      return self._batch_size
  
file_path: agents/tf_agents/bandits/environments/bernoulli_py_environment.py
imports: from typing import Optional, Sequence
  import gin
  import numpy
  from tf_agents.bandits.environments import bandit_py_environment
  from tf_agents.specs import array_spec
  from tf_agents.typing import types
label_desc: Use image node_id 5 for calling the BernoulliPyEnvironment obj's underlying member method code with example usage: obj.batch_size() and returns: self