class_docstr: The convolutional network used to compute the agent's Q-values.
class_docstr_tok: ["The","convolutional","network","used","to","compute","the","agent","'s","Q-values","."]
code: def __call__(self, x, stop_grad_representation=False):
      initializer = nn.initializers.xavier_uniform()
      if not self.inputs_preprocessed:
          x = preprocess_atari_inputs(x)
      logging.info("Creating Nature DQN network")
      x = nn.Conv(
          features=32,
          kernel_size=(8, 8),
          strides=(4, 4),
          kernel_init=initializer,
      )(x)
      x = nn.relu(x)
      x = nn.Conv(
          features=64,
          kernel_size=(4, 4),
          strides=(2, 2),
          kernel_init=initializer,
      )(x)
      x = nn.relu(x)
      x = nn.Conv(
          features=64,
          kernel_size=(3, 3),
          strides=(1, 1),
          kernel_init=initializer,
      )(x)
      x = nn.relu(x)
      x = x.reshape(-1)  # flatten
      x = nn.Dense(features=512, kernel_init=initializer)(x)
      representation = nn.relu(
          x
      )  # Use penultimate layer as representation
      if stop_grad_representation:
          representation = jax.lax.stop_gradient(representation)
      q_values = nn.Dense(
          features=self.num_actions, kernel_init=initializer
      )(representation)
      return NetworkType(q_values, representation)
  
imports: import collections
  from typing import Tuple
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from flax import linen
  import gin
  import jax
  import jax.numpy
label_desc: Use image node_id 1 for calling the JAXDQNNetworkWithRepresentations obj's underlying member method code with example usage: obj.__call__(x, stop_grad_representation) and returns: NetworkType