class_docstr: Model to learn a univariate Gaussian distribution.
  
  Arguments
  ----------
  mu: Mean of the Gaussian distribution
  sigma: Standard deviation of the Gaussian distribution
  device: The torch.device to use, typically cpu or gpu id
class_docstr_tok: ["Model","to","learn","a","univariate","Gaussian","distribution",".","Arguments","--","--","--","--","--","mu",":","Mean","of","the","Gaussian","distribution","sigma",":","Standard","deviation","of","the","Gaussian","distribution","device",":","The","torch.device","to","use",",","typically","cpu","or","gpu","id"]
code: def __init__(self, mu, sigma, device=None):
      super().__init__()
      if device is not None:
          self.device = device
          mu = mu.to(device)
          sigma = sigma.to(device)
      self.mu = mu
      self.sigma = sigma
      self.distr = Normal(self.mu, self.sigma)
  
file_path: gluonts/src/gluonts/nursery/spliced_binned_pareto/gaussian_model.py
imports: import torch
  import torch.nn.functional
  from torch import nn
  from torch.distributions.normal import Normal
label_desc: Use image node_id 1 to create a new GaussianModel object from inherited base classes: nn with example: obj = GaussianModel(mu, sigma, device)