original_docstring: 
          Samples z from a multivariate Gaussian with diagonal covariance matrix using the
           reparameterization trick.
          
docstring: Samples z from a multivariate Gaussian with diagonal covariance matrix using the
  reparameterization trick.
docstring_tokens: Samples z from a multivariate Gaussian with diagonal covariance matrix using the reparameterization trick .
code: def reparameterize(self, mu, var):
          """
          Samples z from a multivariate Gaussian with diagonal covariance matrix using the
           reparameterization trick.
          """
  
          std = var.sqrt()
          eps = self.FloatTensor(std.size()).normal_()
          eps = Variable(eps)
          z = eps.mul(std).add_(mu)
  
          return z
code_tokens: def reparameterize ( self , mu , var ) : std = var . sqrt ( ) eps = self . FloatTensor ( std . size ( ) ) . normal_ ( ) eps = Variable ( eps ) z = eps . mul ( std ) . add_ ( mu ) return z
short_docstring: Samples z from a multivariate Gaussian with diagonal covariance matrix using the
  reparameterization trick.
short_docstring_tokens: Samples z from a multivariate Gaussian with diagonal covariance matrix using the reparameterization trick .
comment: """
          Samples z from a multivariate Gaussian with diagonal covariance matrix using the
           reparameterization trick.
          """