class_docstr: DiscreteQFArgmax policy.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      qf (garage.q_functions.QFunction): The q-function used.
      name (str): Name of the policy.
docstr: Get actions from this policy for the input observations.
  
  Args:
      observations (numpy.ndarray): Observations from environment.
  
  Returns:
      numpy.ndarray: Optimal actions from this policy.
      dict: Predicted action and agent information. It returns an empty
          dict since there is no parameterization.
docstr_tok: ["Get","actions","from","this","policy","for","the","input","observations",".","Args",":","observations","(","numpy.ndarray",")",":","Observations","from","environment",".","Returns",":","numpy.ndarray",":","Optimal","actions","from","this","policy",".","dict",":","Predicted","action","and","agent","information",".","It","returns","an","empty","dict","since","there","is","no","parameterization","."]
code: def get_actions(self, observations):
      """Get actions from this policy for the input observations.
  
      Args:
          observations (numpy.ndarray): Observations from environment.
  
      Returns:
          numpy.ndarray: Optimal actions from this policy.
          dict: Predicted action and agent information. It returns an empty
              dict since there is no parameterization.
  
      """
      if isinstance(
          self.env_spec.observation_space, akro.Image
      ) and len(observations[0].shape) < len(
          self.env_spec.observation_space.shape
      ):
          observations = self.env_spec.observation_space.unflatten_n(
              observations
          )
      q_vals = self._f_qval(observations)
      opt_actions = np.argmax(q_vals, axis=1)
  
      return opt_actions, dict()
  
imports: import akro
  import numpy
  import tensorflow
  from garage.tf.models import Module
  from garage.tf.policies.policy import Policy
label_desc: Use image node_id 4 for calling the DiscreteQFArgmaxPolicy obj's underlying member method code with example usage: obj.get_actions(observations) and returns: opt_actions,  dict