code: def select_action(
      network_def,
      params,
      state,
      rng,
      num_actions,
      eval_mode,
      epsilon_eval,
      epsilon_train,
      epsilon_decay_period,
      training_steps,
      min_replay_history,
      epsilon_fn,
  ):
      """Select an action from the set of available actions.
  
      Chooses an action randomly with probability self._calculate_epsilon(), and
      otherwise acts greedily according to the current Q-value estimates.
  
      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.
        num_actions: int, number of actions (static_argnum).
        eval_mode: bool, whether we are in eval mode (static_argnum).
        epsilon_eval: float, epsilon value to use in eval mode (static_argnum).
        epsilon_train: float, epsilon value to use in train mode (static_argnum).
        epsilon_decay_period: float, decay period for epsilon value for certain
          epsilon functions, such as linearly_decaying_epsilon, (static_argnum).
        training_steps: int, number of training steps so far.
        min_replay_history: int, minimum number of steps in replay buffer
          (static_argnum).
        epsilon_fn: function used to calculate epsilon value (static_argnum).
  
      Returns:
        rng: Jax random number generator.
        action: int, the selected action.
      """
      epsilon = jnp.where(
          eval_mode,
          epsilon_eval,
          epsilon_fn(
              epsilon_decay_period,
              training_steps,
              min_replay_history,
              epsilon_train,
          ),
      )
  
      rng, rng1, rng2 = jax.random.split(rng, num=3)
      p = jax.random.uniform(rng1)
      return rng, jnp.where(
          p <= epsilon,
          jax.random.randint(rng2, (), 0, num_actions),
          jnp.argmax(network_def.apply(params, state).q_values),
      )
  
imports: import collections
  import functools
  import inspect
  import math
  import time
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: select_action(network_def, params, state, rng, num_actions, eval_mode, epsilon_eval, epsilon_train, epsilon_decay_period, training_steps, min_replay_history, epsilon_fn) and returns: rng,  jnp