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 __init__(
      self,
      batch_shape: torch.Size = torch.Size([]),
      noise_prior: Optional[Prior] = None,
      noise_constraint: Optional[Interval] = None,
  ) -> None:
      super().__init__()
  
      if noise_constraint is None:
          noise_constraint = Positive()
  
      self.raw_noise = torch.nn.Parameter(torch.zeros(*batch_shape, 1))
  
      if noise_prior is not None:
          self.register_prior(
              "noise_prior",
              noise_prior,
              lambda m: m.noise,
              lambda m, v: m._set_noise(v),
          )
  
      self.register_constraint("raw_noise", noise_constraint)
  
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 1 to create a new LaplaceLikelihood object from inherited base classes: _OneDimensionalLikelihood with example: obj = LaplaceLikelihood(batch_shape, noise_prior, noise_constraint)