class_docstr: The convolutional network used to compute the agent's Q-values.
code: def __call__(self, x):
      initializer = nn.initializers.xavier_uniform()
      if not self.inputs_preprocessed:
          x = preprocess_atari_inputs(x)
      x = self.encoder(x)
      x = x.reshape((-1))  # flatten
      x = nn.Dense(features=512, kernel_init=initializer)(x)
      x = nn.relu(x)
      q_values = nn.Dense(
          features=self.num_actions, kernel_init=initializer
      )(x)
      return atari_lib.DQNNetworkType(q_values)
  
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 the ImpalaDQNNetwork obj's underlying member method code with example usage: obj.__call__(x) and returns: atari_lib