class_docstr: Half-Normal prior.
  pdf(x) = 2 * (2 * pi * scale^2)^-0.5 * exp(-x^2 / (2 * scale^2)) for x >= 0; 0 for x < 0
  where scale^2 is the variance.
class_docstr_tok: ["Half-Normal","prior",".","pdf","(","x",")","=","2","*","(","2","*","pi","*","scale^2",")","^-0.5","*","exp","(","-x^2","\/","(","2","*","scale^2",")",")","for","x",">","=","0",";","0","for","x","<","0","where","scale^2","is","the","variance","."]
code: def expand(self, batch_shape):
      return HalfNormal(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 HalfNormalPrior obj's underlying member method code with example usage: obj.expand(batch_shape) and returns: HalfNormal