class_docstr: Gaussian head with fixed covariance.
  
  This module is intended to be attached to a neural network that outputs
  the mean of a Gaussian policy. Its covariance is fixed to a diagonal matrix
  with a given scale.
  
  Args:
      scale (float): Scale parameter.
docstr: Return a Gaussian with given mean.
  
  Args:
      mean (torch.Tensor): Batch of mean vectors.
  
  Returns:
      torch.distributions.Distribution: Multivariate Gaussian whose mean
          is the mean argument and whose scale is fixed.
docstr_tok: ["Return","a","Gaussian","with","given","mean",".","Args",":","mean","(","torch.Tensor",")",":","Batch","of","mean","vectors",".","Returns",":","torch.distributions.Distribution",":","Multivariate","Gaussian","whose","mean","is","the","mean","argument","and","whose","scale","is","fixed","."]
code: def forward(self, mean):
      """Return a Gaussian with given mean.
  
      Args:
          mean (torch.Tensor): Batch of mean vectors.
  
      Returns:
          torch.distributions.Distribution: Multivariate Gaussian whose mean
              is the mean argument and whose scale is fixed.
      """
      return torch.distributions.Independent(
          torch.distributions.Normal(loc=mean, scale=self.scale), 1
      )
  
file_path: pfrl/pfrl/policies/gaussian_policy.py
imports: import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the GaussianHeadWithFixedCovariance obj's underlying member method code with example usage: obj.forward(mean) and returns: torch