class_docstr: A Laplace likelihood/noise model for GP regression.
  It has one learnable parameter: :math:`\sigma` - the noise
  
  :param batch_shape: The batch shape of the learned noise parameter (default: []).
  :param noise_prior: Prior for noise parameter :math:`\sigma`.
  :param noise_constraint: Constraint for noise parameter :math:`\sigma`.
  
  :var torch.Tensor noise: :math:`\sigma` parameter (noise)
code: def noise(self) -> Tensor:
      return self.raw_noise_constraint.transform(self.raw_noise)
  
file_path: gpytorch/gpytorch/likelihoods/laplace_likelihood.py
imports: from typing import Any, Optional
  import torch
  from torch import Tensor
  from torch.distributions import Laplace
  from ..constraints import Interval, Positive
  from ..distributions import base_distributions
  from ..priors import Prior
  from .likelihood import _OneDimensionalLikelihood
label_desc: Use image node_id 2 for calling the LaplaceLikelihood obj's underlying member method code with example usage: obj.noise() and returns: self