class_docstr: GaussianMLPModule which has two different mean and std network.
  
  Args:
      input_dim (int): Input dimension of the model.
      output_dim (int): Output dimension of the model.
      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): 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_hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for std. For example, (32, 32) means the MLP consists
          of two hidden layers, each with 32 hidden units.
      std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
          in the std network.
      std_hidden_w_init (callable):  Initializer function for the weight
          of hidden layer (s).
      std_hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s).
      std_output_nonlinearity (callable): Activation function for output
          dense layer in the std network. It should return a torch.Tensor.
          Set it to None to maintain a linear activation.
      std_output_w_init (callable): Initializer function for the weight
          of output dense layer(s) in the std network.
      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.
      normal_distribution_cls (torch.distribution): normal distribution class
          to be constructed and returned by a call to forward. By default, is
          `torch.distributions.Normal`.
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docstr: Default bias initialization function.
  
  Args:
      b (torch.Tensor): The bias tensor.
  
  Returns:
      torch.Tensor: The bias tensor itself.
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code: def _init_std_b(self, b):
      """Default bias initialization function.
  
      Args:
          b (torch.Tensor): The bias tensor.
  
      Returns:
          torch.Tensor: The bias tensor itself.
  
      """
      return nn.init.constant_(b, self._init_std.item())
  
imports: import abc
  import torch
  from torch import nn
  from torch.distributions import Normal
  from torch.distributions.independent import Independent
  from garage.torch.distributions import TanhNormal
  from garage.torch.modules.mlp_module import MLPModule
  from garage.torch.modules.multi_headed_mlp_module import MultiHeadedMLPModule
label_desc: Use image node_id 2 for calling the GaussianMLPIndependentStdModule obj's underlying member method code with example usage: obj._init_std_b(b) and returns: nn