code: def test_batch_separate(self):
      a = torch.tensor(
          [[[4, 1], [2, 2], [8, 0]], [[2, 5], [6, 1], [0, 1]]],
          dtype=torch.float,
      )
      b = torch.tensor(
          [[[0, 0], [2, 1], [1, 0]], [[1, 1], [2, 3], [1, 0]]],
          dtype=torch.float,
      )
      period = torch.tensor([1, 2], dtype=torch.float).view(2, 1, 1)
      kernel = CosineKernel(batch_shape=torch.Size([2])).initialize(
          period_length=period
      )
      kernel.eval()
  
      actual = torch.zeros(2, 3, 3)
      for k in range(2):
          for i in range(3):
              for j in range(3):
                  actual[k, i, j] = torch.cos(
                      math.pi
                      * ((a[k, i] - b[k, j]) / period[k]).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 = 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)
  
      # batch_dims
      actual = torch.zeros(2, 2, 3, 3)
      for k in range(2):
          for i in range(3):
              for j in range(3):
                  for l in range(2):
                      actual[k, l, i, j] = torch.cos(
                          math.pi
                          * ((a[k, i, l] - b[k, j, l]) / period[k])
                      )
      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 = actual.diagonal(dim1=-2, dim2=-1)
      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 3 for calling the TestCosineKernel obj's underlying member method code with example usage: obj.test_batch_separate() without return types