class_docstr: MLP whose outputs are fed into a Normal distribution..
  
  A policy that contains a MLP to make prediction based on a gaussian
  distribution.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for mean. For example, (32, 32) means the MLP consists
          of two hidden layers, each with 32 hidden units.
      hidden_nonlinearity (callable): Activation function for intermediate
          dense layer(s). It should return a torch.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          torch.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          torch.Tensor.
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a torch.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          torch.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          torch.Tensor.
      learn_std (bool): Is std trainable.
      init_std (float): Initial value for std.
          (plain value - not log or exponentiated).
      min_std (float): Minimum value for std.
      max_std (float): Maximum value for std.
      std_parameterization (str): How the std should be parametrized. There
          are two options:
          - exp: the logarithm of the std will be stored, and applied a
             exponential transformation
          - softplus: the std will be computed as log(1+exp(x))
      layer_normalization (bool): Bool for using layer normalization or not.
      name (str): Name of policy.
docstr: Compute the action distributions from the observations.
  
  Args:
      observations (torch.Tensor): Batch of observations on default
          torch device.
  
  Returns:
      torch.distributions.Distribution: Batch distribution of actions.
      dict[str, torch.Tensor]: Additional agent_info, as torch Tensors
docstr_tok: ["Compute","the","action","distributions","from","the","observations",".","Args",":","observations","(","torch.Tensor",")",":","Batch","of","observations","on","default","torch","device",".","Returns",":","torch.distributions.Distribution",":","Batch","distribution","of","actions",".","dict","[","str",",","torch.Tensor","]",":","Additional","agent_info",",","as","torch","Tensors"]
code: def forward(self, observations):
      """Compute the action distributions from the observations.
  
      Args:
          observations (torch.Tensor): Batch of observations on default
              torch device.
  
      Returns:
          torch.distributions.Distribution: Batch distribution of actions.
          dict[str, torch.Tensor]: Additional agent_info, as torch Tensors
  
      """
      dist = self._module(observations)
      return (
          dist,
          dict(mean=dist.mean, log_std=(dist.variance**0.5).log()),
      )
  
imports: import torch
  from torch import nn
  from garage.torch.modules import GaussianMLPModule
  from garage.torch.policies.stochastic_policy import StochasticPolicy
label_desc: Use image node_id 2 for calling the GaussianMLPPolicy obj's underlying member method code with example usage: obj.forward(observations) and returns: dist,  dict