code: def test_computes_periodic_function(self):
      a = torch.tensor([[4, 1], [2, 2], [8, 0]], dtype=torch.float)
      b = torch.tensor([[0, 0], [2, 1], [1, 0]], dtype=torch.float)
      period = 1
      kernel = CosineKernel().initialize(period_length=period)
      kernel.eval()
  
      actual = torch.zeros(3, 3)
      for i in range(3):
          for j in range(3):
              actual[i, j] = torch.cos(
                  math.pi * ((a[i] - b[j]) / period).norm(2, dim=-1)
              )
  
      res = kernel(a, b).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-5)
  
      # diag
      res = kernel(a, b).diagonal(dim1=-1, dim2=-2)
      actual = actual.diagonal(dim1=-1, dim2=-2)
      self.assertLess(torch.norm(res - actual), 1e-5)
  
      # batch_dims
      actual = torch.zeros(2, 3, 3)
      for i in range(3):
          for j in range(3):
              for l in range(2):
                  actual[l, i, j] = torch.cos(
                      math.pi * ((a[i, l] - b[j, l]) / period)
                  )
      res = kernel(a, b, last_dim_is_batch=True).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-5)
  
      # batch_dims + diag
      res = kernel(a, b, last_dim_is_batch=True).diagonal(
          dim1=-1, dim2=-2
      )
      actual = torch.cat(
          [
              actual[i].diagonal(dim1=-1, dim2=-2).unsqueeze(0)
              for i in range(actual.size(0))
          ]
      )
      self.assertLess(torch.norm(res - actual), 1e-5)
  
file_path: gpytorch/test/kernels/test_cosine_kernel.py
imports: import math
  import unittest
  import torch
  from gpytorch.kernels import CosineKernel
  from gpytorch.priors import NormalPrior
label_desc: Use image node_id 1 for calling the TestCosineKernel obj's underlying member method code with example usage: obj.test_computes_periodic_function() without return types