class_docstr: An environment based on an arbitrary classification problem.
class_docstr_tok: ["An","environment","based","on","an","arbitrary","classification","problem","."]
code: def _apply_action(
      self, action: types.NestedTensor
  ) -> types.NestedTensor:
      action = tf.reshape(
          action, shape=[self._batch_size] + self._action_spec.shape
      )
      reward_samples = self._reward_distribution.sample(
          tf.shape(action)
      )
      return _batched_table_lookup(
          reward_samples, self._current_label, action
      )
  
file_path: agents/tf_agents/bandits/environments/classification_environment.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  import gin
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.environments import bandit_tf_environment
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import eager_utils
label_desc: Use image node_id 3 for calling the ClassificationBanditEnvironment obj's underlying member method code with example usage: obj._apply_action(action) and returns: _batched_table_lookup