class_docstr: A JAX implementation of the SAC agent.
class_docstr_tok: ["A","JAX","implementation","of","the","SAC","agent","."]
docstr: Returns the agent's first action for this episode.
  
  Args:
    observation: numpy array, the environment's initial observation.
  
  Returns:
    np.ndarray, the selected action.
docstr_tok: ["Returns","the","agent","'s","first","action","for","this","episode",".","Args",":","observation",":","numpy","array",",","the","environment","'s","initial","observation",".","Returns",":","np.ndarray",",","the","selected","action","."]
code: def begin_episode(self, observation):
      """Returns the agent's first action for this episode.
  
      Args:
        observation: numpy array, the environment's initial observation.
  
      Returns:
        np.ndarray, the selected action.
      """
      self._reset_state()
      self._record_observation(observation)
  
      if not self.eval_mode:
          self._train_step()
  
      if self._replay.add_count > self.min_replay_history:
          self._rng, self.action = select_action(
              self.network_def,
              self.network_params,
              self.state,
              self._rng,
              self.eval_mode,
          )
      else:
          self._rng, action_rng = jax.random.split(self._rng)
          self.action = jax.random.uniform(
              action_rng,
              self.action_shape,
              self.action_dtype,
              self.action_limits[0],
              self.action_limits[1],
          )
      self.action = onp.asarray(self.action)
      return self.action
  
imports: import functools
  import math
  import operator
  import time
  from typing import Any, Mapping, Tuple
  from absl import logging
  from dopamine.jax import continuous_networks
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  import dopamine.labs.sac_from_pixels.continuous_networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  import flax
  from flax import linen
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 5 for calling the SACAgent obj's underlying member method code with example usage: obj.begin_episode(observation) and returns: self