code: def _generate_training_experience(
      self, accepts_per_arm_features: bool
  ) -> types.NestedTensor:
      batch_size = 240
      observations = self._generate_observations(
          batch_size, accepts_per_arm_features
      )
      num_actions = self._num_actions(accepts_per_arm_features)
      actions = np.tile(
          np.array(range(num_actions), dtype=np.int32),
          int(batch_size / num_actions),
      )
      rewards = self._generate_reward(
          observations, actions, accepts_per_arm_features
      )
      experience = trajectory.single_step(
          observation=observations,
          action=actions,
          policy_info=self._get_policy_info(
              batch_size, accepts_per_arm_features
          ),
          reward=rewards,
          discount=tf.zeros([batch_size]),
      )
      experience = tf.nest.map_structure(
          lambda t: tf.expand_dims(t, axis=1), experience
      )
      return experience
  
imports: from typing import Optional
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.agents import neural_falcon_agent
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
label_desc: Use image node_id 8 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj._generate_training_experience(accepts_per_arm_features) and returns: experience