code: def test_computes_linear_function_square_batch(self):
      a = torch.tensor(
          [[[4, 1], [2, 0], [8, 3]], [[1, 1], [2, 1], [1, 3]]],
          dtype=torch.float,
      )
  
      kernel = LinearKernel().initialize(variance=1.0)
      kernel.eval()
      actual = torch.matmul(a, a.transpose(-1, -2))
      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 = 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)
  
      # batch_dims
      dim_group_a = a
      dim_group_a = dim_group_a.transpose(-1, -2).unsqueeze(-1)
      actual = dim_group_a.matmul(dim_group_a.transpose(-2, -1))
      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 = actual.diagonal(dim1=-2, dim2=-1)
      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 4 for calling the TestLinearKernel obj's underlying member method code with example usage: obj.test_computes_linear_function_square_batch() without return types