code: def compute_optimal_reward(
      observation: np.ndarray,
      delta: float,
      mu_inside: float,
      mu_high: float,
  ) -> types.Array:
      is_inside = np.float32(
          np.linalg.norm(observation, ord=2, axis=1) <= delta
      )
      return is_inside * mu_inside + (1 - is_inside) * mu_high
  
file_path: agents/tf_agents/bandits/environments/wheel_py_environment.py
imports: from __future__ import absolute_import
  from typing import Optional, Sequence, Text
  import gin
  import numpy
  from tf_agents.bandits.environments import bandit_py_environment
  from tf_agents.specs import array_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
label_desc: Use image node_id 2 for calling a global function with example usage: compute_optimal_reward(observation, delta, mu_inside, mu_high) and returns: unknown