class_docstr: Base class for bandit environments implemented in TensorFlow.
  
  Subclasses should implement the `_apply_action` and `_observe` methods.
  
  Example usage with eager mode:
  ```
    # reset() creates the initial time_step and resets the environment.
    time_step = environment.reset()
    for _ in tf.range(num_steps):
      action_step = policy.action(time_step)
      time_step = environment.step(action_step.action)
  ```
  
  Example usage with graph mode:
  ```
    # current_time_step() creates the initial TimeStep.
    time_step = environment.current_time_step()
    action_step = policy.action(time_step)
    # Apply the action and return the new TimeStep.
    next_time_step = environment.step(action_step.action)
  
    sess.run([time_step, action_step, next_time_step])
  ```
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code: def name(self) -> Optional[Text]:
      return self._name
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  from typing import Optional, Text
  import six
  import tensorflow
  from tf_agents.environments import tf_environment
  from tf_agents.trajectories import time_step
  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 8 for calling the BanditTFEnvironment obj's underlying member method code with example usage: obj.name() and returns: self