class_docstr: Multiheaded MLP whose outputs are fed into a TanhNormal distribution.
  
  A policy that contains a MLP to make prediction based on a gaussian
  distribution with a tanh transformation.
  
  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.
      init_std (float): Initial value for std.
          (plain value - not log or exponentiated).
      min_std (float): If not None, the std is at least the value of min_std,
          to avoid numerical issues (plain value - not log or exponentiated).
      max_std (float): If not None, the std is at most the value of max_std,
          to avoid numerical issues (plain value - not log or exponentiated).
      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.
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
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)
      ret_mean = dist.mean.cpu()
      ret_log_std = (dist.variance.sqrt()).log().cpu()
      return dist, dict(mean=ret_mean, log_std=ret_log_std)
  
file_path: garage/src/garage/torch/policies/tanh_gaussian_mlp_policy.py
imports: import numpy
  from torch import nn
  from garage.torch.distributions import TanhNormal
  from garage.torch.modules import GaussianMLPTwoHeadedModule
  from garage.torch.policies.stochastic_policy import StochasticPolicy
label_desc: Use image node_id 2 for calling the TanhGaussianMLPPolicy obj's underlying member method code with example usage: obj.forward(observations) and returns: dist,  dict