class_docstr: Normal (Gaussian) Prior
  
  pdf(x) = (2 * pi * sigma^2)^-0.5 * exp(-(x - mu)^2 / (2 * sigma^2))
  
  where mu is the mean and sigma^2 is the variance.
class_docstr_tok: ["Normal","(","Gaussian",")","Prior","pdf","(","x",")","=","(","2","*","pi","*","sigma^2",")","^-0.5","*","exp","(","-","(","x","-","mu",")","^2","\/","(","2","*","sigma^2",")",")","where","mu","is","the","mean","and","sigma^2","is","the","variance","."]
code: def expand(self, batch_shape):
      batch_shape = torch.Size(batch_shape)
      return NormalPrior(
          self.loc.expand(batch_shape), self.scale.expand(batch_shape)
      )
  
imports: import torch
  from torch.distributions import Gamma, HalfCauchy, HalfNormal, LogNormal, MultivariateNormal, Normal, Uniform
  from torch.nn import Module
  from .prior import Prior
  from .utils import _bufferize_attributes, _del_attributes
label_desc: Use image node_id 2 for calling the NormalPrior obj's underlying member method code with example usage: obj.expand(batch_shape) and returns: NormalPrior