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 forward(
      self, function_samples: Tensor, *args: Any, **kwargs: Any
  ) -> Laplace:
      return base_distributions.Laplace(
          loc=function_samples, scale=self.noise.sqrt()
      )
  
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 5 for calling the LaplaceLikelihood obj's underlying member method code with example usage: obj.forward(function_samples) and returns: base_distributions