class_docstr: Gaussian head with diagonal covariance.
  
  This module is intended to be attached to a neural network that outputs
  a vector that is twice the size of an action vector. The vector is split
  and interpreted as the mean and diagonal covariance of a Gaussian policy.
  
  Args:
      var_func (callable): Callable that computes the variance
          from the second input. It should always return positive values.
code: def __init__(self, var_func=nn.functional.softplus):
      super().__init__()
      self.var_func = var_func
  
file_path: pfrl/pfrl/policies/gaussian_policy.py
imports: import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new GaussianHeadWithDiagonalCovariance object from inherited base classes: nn with example: obj = GaussianHeadWithDiagonalCovariance(var_func)