code: def _mock_outputs(online_params, target_params, key, target_name):
      """Returns mock network outputs."""
      _, policy_params_fn = hk.transform(_hk_mock_policy_params)
      key_seq = hk.PRNGSequence(key)
  
      state_size = ACTION_DIM
  
      # Input state: [TIME_DIM, BATCH_DIM, DIM_STATE]
      s_tm1 = jax.random.normal(
          next(key_seq), (TIME_DIM, BATCH_DIM, state_size), jnp.float32
      )
      policy_params = policy_params_fn(online_params, None, s_tm1)
      target_policy_params = policy_params_fn(
          target_params, None, s_tm1
      )
  
      # Shape for actions: [NUM_SAMPLES, TIME_DIM, BATCH_DIM, ACTION_DIM]
      mean, stddev = (
          target_policy_params["mean"],
          target_policy_params["stddev"],
      )
      mean_repeated = jnp.repeat(
          mean.reshape((1,) + mean.shape), NUM_SAMPLES, axis=0
      )
      stddev_repeated = jnp.repeat(
          stddev.reshape((1,) + stddev.shape), NUM_SAMPLES, axis=0
      )
      target_actions = _DIAGONAL_GAUSSIAN_DIST.sample(
          next(key_seq), mean_repeated, stddev_repeated
      )
      # If the target is advantages then num samples is 1.
      if target_name == "advantages":
          target_actions = target_actions[0, ...]
  
      # Shape for Q: [NUM_SAMPLES, TIME_DIM, BATCH_DIM]
      # Setting Q = -a_t * tf.transpose(a_t) where a_t = s_t + a.
      # The solution to optimizing this is basically for the policy to output
      # 0 actions thereby minimizing the cost. Since this is a convex
      # optimization problem, the algorithm should get to a good solution quickly.
  
      # First compute a_t = s_t + a with shape: [NUM_SAMPLES, TIME_DIM, BATCH_DIM,
      # ACTION_DIM] since action dim is the same as shape dim here and then compute
      # the quadratic form.
      a_t = target_actions + jnp.expand_dims(s_tm1, 0)
      sample_q_values = -jnp.sum(a_t**2, axis=-1)
      # Set the advantage to the same as the q value.
      # Shape for advantages: [TIME_DIM, BATCH_DIM]
      advantages = sample_q_values[0, :, :]
  
      return dict(
          pi_params=policy_params,
          target_pi_params=target_policy_params,
          sample_q_values=sample_q_values,
          advantages=advantages,
          target_actions=target_actions,
      )
  
imports: import functools
  import math
  from typing import Tuple
  from absl.testing import absltest
  from absl.testing import parameterized
  import chex
  import haiku
  import jax
  import jax.numpy
  import numpy
  import optax
  from rlax._src import distributions
  from rlax._src import mpo_ops
label_desc: Use image node_id 3 for calling a global function with example usage: _mock_outputs(online_params, target_params, key, target_name) and returns: dict