code: def _get_replay_buffer(
      data_spec, batch_size, steps_per_loop, async_steps_per_loop
  ):
      """Return a `TFUniformReplayBuffer` for the given `agent`."""
      return bandit_replay_buffer.BanditReplayBuffer(
          data_spec=data_spec,
          batch_size=batch_size,
          max_length=steps_per_loop * async_steps_per_loop,
      )
  
file_path: agents/tf_agents/bandits/agents/examples/v2/trainer.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import logging
  import tensorflow
  from tf_agents.bandits.replay_buffers import bandit_replay_buffer
  from tf_agents.drivers import dynamic_step_driver
  from tf_agents.eval import metric_utils
  from tf_agents.metrics import export_utils
  from tf_agents.metrics import tf_metrics
  from tf_agents.policies import policy_saver
label_desc: Use image node_id 1 for calling a global function with example usage: _get_replay_buffer(data_spec, batch_size, steps_per_loop, async_steps_per_loop) and returns: bandit_replay_buffer