class_docstr: Implementation of WhiteNoise kernel:
  
      :math:`k(x, z) = \sigma^2 \delta(x, z),`
  
  where :math:`\delta` is a Dirac delta function.
code: def __init__(self, input_dim, variance=None, active_dims=None):
      super().__init__(input_dim, active_dims)
  
      variance = torch.tensor(1.0) if variance is None else variance
      self.variance = PyroParam(variance, constraints.positive)
  
file_path: pyro/pyro/contrib/gp/kernels/static.py
imports: import torch
  from torch.distributions import constraints
  from pyro.contrib.gp.kernels.kernel import Kernel
  from pyro.nn.module import PyroParam
label_desc: Use image node_id 1 to create a new WhiteNoise object from inherited base classes: Kernel with example: obj = WhiteNoise(input_dim, variance, active_dims)