code: def generate_random_data(self, num_actions):
      data_len = 256  # equal to one default-sized batch
      state_shape = (84, 84, 4)
      states = np.random.randint(
          0, 255, size=((data_len,) + state_shape)
      )
      actions = np.random.choice(num_actions, size=data_len)
      old_log_probs = np.random.random(size=data_len)
      returns = np.random.random(size=data_len)
      advantages = np.random.random(size=data_len)
      return states, actions, old_log_probs, returns, advantages
  
imports: from absl.testing import absltest
  from flax.training import train_state
  import jax
  import ml_collections
  import numpy
  import numpy.testing
  import agent
  import env_utils
  import models
  import ppo_lib
label_desc: Use image node_id 1 for calling the TestOptimizationStep obj's underlying member method code with example usage: obj.generate_random_data(num_actions) and returns: states,  actions,  old_log_probs,  returns,  advantages