code: def test_computes_cubic_kernel(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)
      kernel = PolynomialKernel(power=3)
      kernel.eval()
  
      actual = torch.zeros(3, 3)
      for i in range(3):
          for j in range(3):
              actual[i, j] = (a[i].matmul(b[j]) + kernel.offset).pow(
                  kernel.power
              )
  
      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(2):
          actual[i] = kernel(
              a[:, i].unsqueeze(-1), b[:, i].unsqueeze(-1)
          ).to_dense()
  
      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_polynomial_kernel.py
imports: import unittest
  import torch
  from gpytorch.kernels import PolynomialKernel
  from gpytorch.priors import NormalPrior
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 3 for calling the TestPolynomialKernel obj's underlying member method code with example usage: obj.test_computes_cubic_kernel() without return types