class_docstr: A convolutional value and policy networks for SAC.
  
  This uses the SAC-AE network for processing images. For more information,
  view SACEncoderNetwork.
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docstr: Calls the SAC critic network.
  
  SAC uses two Q networks to reduce overestimation bias.
  This can be called using network_def.apply(..., method=network_def.critic).
  
  Args:
    state: An input state.
    action: An action to compute the value of.
  
  Returns:
    A named tuple containing the Q values of each network.
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code: def critic(
      self, state: jnp.ndarray, action: jnp.ndarray
  ) -> continuous_networks.SacCriticOutput:
      """Calls the SAC critic network.
  
      SAC uses two Q networks to reduce overestimation bias.
      This can be called using network_def.apply(..., method=network_def.critic).
  
      Args:
        state: An input state.
        action: An action to compute the value of.
  
      Returns:
        A named tuple containing the Q values of each network.
      """
      encoding = self._encoder(state)
      return self._sac_network.critic(encoding.critic_z, action)
  
file_path: dopamine/dopamine/labs/sac_from_pixels/continuous_networks.py
imports: import dataclasses
  from typing import Optional, Tuple
  from dopamine.jax import continuous_networks
  from flax import linen
  import gin
  import jax
  from jax import numpy
label_desc: Use image node_id 4 for calling the SACConvNetwork obj's underlying member method code with example usage: obj.critic(state, action) and returns: self