code: def select_action(network_def, params, state, rng, eval_mode=False):
      """Sample an action to take from the current policy network.
  
      This obtains a mean and variance from the input policy network, and samples an
      action using a Gaussian distribution.
  
      Args:
        network_def: Linen Module to use for inference.
        params: Linen params (frozen dict) to use for inference.
        state: input state to use for inference.
        rng: Jax random number generator.
        eval_mode: bool, whether in eval mode.
  
      Returns:
        rng: Jax random number generator.
        action: int, the selected action.
      """
      rng, rng2 = jax.random.split(rng)
      greedy_a, sampled_a, _ = network_def.apply(
          params, state, rng2, method=network_def.actor
      )
      return rng, jnp.where(eval_mode, greedy_a, sampled_a)
  
imports: import functools
  import math
  import operator
  import time
  from typing import Any, Mapping, Tuple
  from absl import logging
  from dopamine.jax import continuous_networks
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  import dopamine.labs.sac_from_pixels.continuous_networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  import flax
  from flax import linen
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: select_action(network_def, params, state, rng, eval_mode) and returns: rng,  jnp