code: def feature_layer(key, noisy, eval_mode=False):
      """Network feature layer depending on whether noisy_nets are used on or not."""
  
      def noisy_net(x, features):
          return NoisyNetwork(rng_key=key, eval_mode=eval_mode)(
              x, features
          )
  
      def dense_net(x, features):
          return nn.Dense(
              features, kernel_init=nn.initializers.xavier_uniform()
          )(x)
  
      return noisy_net if noisy else dense_net
  
imports: import itertools
  import time
  from typing import Optional, Sequence, Tuple, Union
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from flax import linen
  import gin
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: feature_layer(key, noisy, eval_mode) and returns: unknown, NoisyNetwork, nn