code: def test_computes_linear_function_square(self):
      a = torch.tensor([[4, 1], [2, 0], [8, 3]], dtype=torch.float)
  
      kernel = LinearKernel().initialize(variance=3.14)
      kernel.eval()
      actual = torch.matmul(a, a.t()) * 3.14
      res = kernel(a, a).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-4)
  
      # diag
      res = kernel(a, a).diagonal(dim1=-1, dim2=-2)
      actual = actual.diagonal(dim1=-1, dim2=-2)
      self.assertLess(torch.norm(res - actual), 1e-4)
  
      # batch_dims
      dim_group_a = a
      dim_group_a = dim_group_a.permute(1, 0).reshape(-1, 3)
      actual = 3.14 * torch.mul(
          dim_group_a.unsqueeze(-1), dim_group_a.unsqueeze(-2)
      )
      res = kernel(a, a, last_dim_is_batch=True).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-4)
  
      # batch_dims + diag
      res = kernel(a, a, 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-4)
  
file_path: gpytorch/test/kernels/test_linear_kernel.py
imports: import unittest
  import torch
  from gpytorch.kernels import LinearKernel
  from gpytorch.priors import NormalPrior
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 3 for calling the TestLinearKernel obj's underlying member method code with example usage: obj.test_computes_linear_function_square() without return types