class_docstr: A RL2 policy after adaptation.
  
  Args:
      policy (garage.tf.policies.Policy): Policy itself.
class_docstr_tok: ["A","RL2","policy","after","adaptation",".","Args",":","policy","(","garage.tf.policies.Policy",")",":","Policy","itself","."]
docstr: Get a single action from this policy for the input observation.
  
  Args:
      obs (numpy.ndarray): Observation from environment.
  
  Returns:
      numpy.ndarray: Predicated action
      dict: Agent info.
docstr_tok: ["Get","a","single","action","from","this","policy","for","the","input","observation",".","Args",":","obs","(","numpy.ndarray",")",":","Observation","from","environment",".","Returns",":","numpy.ndarray",":","Predicated","action","dict",":","Agent","info","."]
code: def get_action(self, obs):
      """Get a single action from this policy for the input observation.
  
      Args:
          obs (numpy.ndarray): Observation from environment.
  
      Returns:
          numpy.ndarray: Predicated action
          dict: Agent info.
  
      """
      return self._policy.get_action(obs)
  
imports: import abc
  import collections
  import akro
  from dowel import logger
  import numpy
  from garage import EnvSpec, EnvStep, EpisodeBatch, log_multitask_performance, StepType, Wrapper
  from garage.np.algos import MetaRLAlgorithm
  from garage.sampler import DefaultWorker
  from garage.tf.algos._rl2npo import RL2NPO
label_desc: Use image node_id 3 for calling the RL2AdaptedPolicy obj's underlying member method code with example usage: obj.get_action(obs) and returns: self