class_docstr: A smoothed approximation of a uniform prior.
  
  Has full support on the reals and is differentiable everywhere.
  
  .. math::
  
      \begin{equation*}
          B = {x: a_i <= x_i <= b_i}
          d(x, B) = min_{x' in B} |x - x'|
          pdf(x) \sim exp(- d(x, B)**2 / sqrt(2 * sigma^2))
      \end{equation*}
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code: def _M(self):
      # normalization factor to make this a probability distribution
      return torch.log(
          1 + (self.b - self.a) / (math.sqrt(2 * math.pi) * self.sigma)
      )
  
file_path: gpytorch/gpytorch/priors/smoothed_box_prior.py
imports: import math
  from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import broadcast_all
  from torch.nn import Module
  from .prior import Prior
  from .torch_priors import NormalPrior
label_desc: Use image node_id 4 for calling the SmoothedBoxPrior obj's underlying member method code with example usage: obj._M() and returns: torch