code: def test_kernel_with_expand(self):
      def f(a):
          return a.expand((2, 3, 4))
  
      device = "cpu"
      x = torch.rand((1, 3, 1), device=device)
      graph_str = """
  graph(%a : Float(1, 3, 1, strides=[3, 1, 1], requires_grad=0, device=cpu)):
    %1 : int = prim::Constant[value=2]()
    %2 : int = prim::Constant[value=3]()
    %3 : int = prim::Constant[value=4]()
    %4 : int[] = prim::ListConstruct(%1, %2, %3)
    %5 : bool = prim::Constant[value=0]()
    %6 : Float(2, 3, 4, strides=[12, 4, 0], requires_grad=0, device=cpu) = aten::expand(%a, %4, %5)
    return (%6)
          """
      graph = torch._C.parse_ir(graph_str)
  
      kernel = te.TensorExprKernel(graph)
      res1 = kernel.run((x,))
      res2 = kernel.fallback((x,))
      correct = f(x)
      np.testing.assert_allclose(
          res1.numpy(), correct.numpy(), atol=2e-3
      )
      np.testing.assert_allclose(
          res2.numpy(), correct.numpy(), atol=2e-3
      )
  
imports: import torch
  import numpy
  import torch._C._te
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.jit_utils import JitTestCase
  import unittest
label_desc: Use image node_id 15 for calling the TestTensorExprPyBind obj's underlying member method code with example usage: obj.test_kernel_with_expand() and returns: a