class_docstr: A JAX implementation of the DQN agent.
class_docstr_tok: ["A","JAX","implementation","of","the","DQN","agent","."]
code: def _sample_from_replay_buffer(self):
      samples = self._replay.sample_transition_batch()
      types = self._replay.get_transition_elements()
      self.replay_elements = collections.OrderedDict()
      for element, element_type in zip(samples, types):
          self.replay_elements[element_type.name] = element
  
imports: import collections
  import functools
  import inspect
  import math
  import time
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 4 for calling the JaxDQNAgent obj's underlying member method code with example usage: obj._sample_from_replay_buffer() without return types