class_docstr: Normal distribution with additive identically distributed (i.i.d.)
  uniform noise.
  
  The method is described in Appendix 6.2. of:
  
      | “Variational Image Compression with a Scale Hyperprior”
      | Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang,
          Nick Johnston
      | https://arxiv.org/abs/1802.01436
  
  Args:
      loc: mean of the distribution.
      scale: standard deviation of the distribution.
code: def __init__(
      self,
      loc: Union[float, Tensor],
      scale: Union[float, Tensor],
  ):
      distribution = Normal(loc, scale)
  
      super(NoisyNormal, self).__init__(distribution)
  
imports: from typing import Union
  from torch import Tensor
  from torch.distributions import Normal
  from ._uniform_noise import UniformNoise
label_desc: Use image node_id 1 to create a new NoisyNormal object from inherited base classes: UniformNoise with example: obj = NoisyNormal(loc, scale)