text: """GaussianMLPPolicy."""
  import torch
  
  from garage.torch.modules import GaussianMLPModule
  from garage.torch.policies import Policy
  
  
  class GaussianMLPPolicy(Policy, GaussianMLPModule):
      """
      GaussianMLPPolicy.
  
      A policy that contains a MLP to make prediction based on a gaussian
      distribution.
  
      Args:
          env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
          module : GaussianMLPModule to make prediction based on a gaussian
          distribution.
      :return:
      """
  
      def __init__(self, env_spec, **kwargs):
          self._obs_dim = env_spec.observation_space.flat_dim
          self._action_dim = env_spec.action_space.flat_dim
  
          Policy.__init__(self, env_spec)
          GaussianMLPModule.__init__(self,
                                     input_dim=self._obs_dim,
                                     output_dim=self._action_dim,
                                     **kwargs)
  
      def forward(self, inputs):
          """Forward method."""
          return super().forward(torch.Tensor(inputs))
  
      def get_action(self, observation):
          """Get a single action given an observation."""
          with torch.no_grad():
              observation = observation.unsqueeze(0)
              dist = self.forward(observation)
              return (dist.rsample().squeeze(0).numpy(),
                      dict(mean=dist.mean.squeeze(0).numpy(),
                           log_std=(dist.variance**.5).log().squeeze(0).numpy()))
  
      def get_actions(self, observations):
          """Get actions given observations."""
          with torch.no_grad():
              dist = self.forward(observations)
              return (dist.rsample().numpy(),
                      dict(mean=dist.mean.numpy(),
                           log_std=(dist.variance**.5).log().numpy()))
  
      def log_likelihood(self, observation, action):
          """Get log likelihood given observations and action."""
          dist = self.forward(observation)
          return dist.log_prob(action)
  
      def get_entropy(self, observation):
          """Get entropy given observations."""
          dist = self.forward(observation)
          return dist.entropy()
  
      def reset(self, dones=None):
          """Reset the environment."""
          pass
  
      @property
      def vectorized(self):
          """Vectorized or not."""
          return True
  