code: def _generate_reward(
      self,
      observations: types.NestedTensor,
      actions: types.Tensor,
      accepts_per_arm_features: bool,
  ) -> types.Tensor:
      if accepts_per_arm_features:
          global_obs = observations[
              bandit_spec_utils.GLOBAL_FEATURE_KEY
          ]
          arm_obs = observations[bandit_spec_utils.PER_ARM_FEATURE_KEY]
          return tf.reduce_sum(
              global_obs
              * tf.gather(
                  params=arm_obs, indices=actions, batch_dims=1
              ),
              axis=1,
          )
      else:
          return tf.gather(
              params=observations, indices=actions, batch_dims=1
          )
  
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 6 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj._generate_reward(observations, actions, accepts_per_arm_features) and returns: tf, tf