class_docstr: Implementation of Periodic kernel:
  
      :math:`k(x,z)=\sigma^2\exp\left(-2\times\frac{\sin^2(\pi(x-z)/p)}{l^2}\right),`
  
  where :math:`p` is the ``period`` parameter.
  
  References:
  
  [1] `Introduction to Gaussian processes`,
  David J.C. MacKay
  
  :param torch.Tensor lengthscale: Length scale parameter of this kernel.
  :param torch.Tensor period: Period parameter of this kernel.
code: def forward(self, X, Z=None, diag=False):
      if diag:
          return self.variance.expand(X.size(0))
  
      if Z is None:
          Z = X
      X = self._slice_input(X)
      Z = self._slice_input(Z)
      if X.size(1) != Z.size(1):
          raise ValueError(
              "Inputs must have the same number of features."
          )
  
      d = X.unsqueeze(1) - Z.unsqueeze(0)
      scaled_sin = (
          torch.sin(math.pi * d / self.period) / self.lengthscale
      )
      return self.variance * torch.exp(-2 * (scaled_sin**2).sum(-1))
  
file_path: pyro/pyro/contrib/gp/kernels/periodic.py
imports: import math
  import torch
  from torch.distributions import constraints
  from pyro.contrib.gp.kernels.isotropic import Isotropy
  from pyro.contrib.gp.kernels.kernel import Kernel
  from pyro.nn.module import PyroParam
label_desc: Use image node_id 2 for calling the Periodic obj's underlying member method code with example usage: obj.forward(X, Z, diag) and returns: unknown, self