code: def feature_layer(noisy, features):
      """Network feature layer depending on whether noisy_nets are used on or not."""
      if noisy:
          net = NoisyNetwork(features=features)
      else:
          net = nn.Dense(
              features, kernel_init=nn.initializers.xavier_uniform()
          )
  
      def apply(x, key, eval_mode):
          if noisy:
              return net(
                  x, key, True, None, eval_mode
              )  # pytype: disable=wrong-arg-count
          else:
              return net(x)
  
      return net, apply
  
imports: import collections
  import functools
  import time
  from typing import Any, Callable, Optional, Tuple
  from dopamine.jax.networks import preprocess_atari_inputs
  from flax import linen
  import gin
  import jax
  from jax import lax
  from jax import random
  from jax.nn import initializers
  import jax.numpy
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: feature_layer(noisy, features) and returns: net,  apply, net, net