class_docstr: Policy that output random action uniformly.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
class_docstr_tok: ["Policy","that","output","random","action","uniformly",".","Args",":","env_spec","(","garage.envs.env_spec.EnvSpec",")",":","Environment","specification","."]
docstr: Get single action from this policy for the input observation.
  
  Args:
      observation (numpy.ndarray): Observation from environment.
  
  Returns:
      numpy.ndarray: Action
      dict: Predicted action and agent information. It returns an empty
          dict since there is no parameterization.
docstr_tok: ["Get","single","action","from","this","policy","for","the","input","observation",".","Args",":","observation","(","numpy.ndarray",")",":","Observation","from","environment",".","Returns",":","numpy.ndarray",":","Action","dict",":","Predicted","action","and","agent","information",".","It","returns","an","empty","dict","since","there","is","no","parameterization","."]
code: def get_action(self, observation):
      """Get single action from this policy for the input observation.
  
      Args:
          observation (numpy.ndarray): Observation from environment.
  
      Returns:
          numpy.ndarray: Action
          dict: Predicted action and agent information. It returns an empty
              dict since there is no parameterization.
  
      """
      return self.action_space.sample(), dict()
  
imports: from garage.tf.policies.policy import Policy
label_desc: Use image node_id 2 for calling the UniformControlPolicy obj's underlying member method code with example usage: obj.get_action(observation) and returns: self,  dict