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.
code: def __init__(
      self,
      env_spec,
      hidden_sizes=(32, 32),
      hidden_nonlinearity=nn.ReLU,
      hidden_w_init=nn.init.xavier_uniform_,
      hidden_b_init=nn.init.zeros_,
      output_nonlinearity=None,
      output_w_init=nn.init.xavier_uniform_,
      output_b_init=nn.init.zeros_,
      init_std=1.0,
      min_std=np.exp(-20.0),
      max_std=np.exp(2.0),
      std_parameterization="exp",
      layer_normalization=False,
  ):
      super().__init__(env_spec, name="TanhGaussianPolicy")
  
      self._obs_dim = env_spec.observation_space.flat_dim
      self._action_dim = env_spec.action_space.flat_dim
  
      self._module = GaussianMLPTwoHeadedModule(
          input_dim=self._obs_dim,
          output_dim=self._action_dim,
          hidden_sizes=hidden_sizes,
          hidden_nonlinearity=hidden_nonlinearity,
          hidden_w_init=hidden_w_init,
          hidden_b_init=hidden_b_init,
          output_nonlinearity=output_nonlinearity,
          output_w_init=output_w_init,
          output_b_init=output_b_init,
          init_std=init_std,
          min_std=min_std,
          max_std=max_std,
          std_parameterization=std_parameterization,
          layer_normalization=layer_normalization,
          normal_distribution_cls=TanhNormal,
      )
  
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 1 to create a new TanhGaussianMLPPolicy object from inherited base classes: StochasticPolicy with example: obj = TanhGaussianMLPPolicy(env_spec, hidden_sizes, hidden_nonlinearity, hidden_w_init, hidden_b_init, output_nonlinearity, output_w_init, output_b_init, init_std, min_std, max_std, std_parameterization, layer_normalization)