code: def test_kernel_with_tensor_inputs(self):
      def f(a, b, c):
          return a + b + c
  
      device, size = "cpu", (4, 4)
      x = torch.rand(size, device=device)
      y = torch.rand(size, device=device)
      z = torch.rand(size, device=device)
  
      graph_str = """
  graph(%a.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu),
        %b.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu),
        %c.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu)):
    %6 : int = prim::Constant[value=1]()
    %7 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) = aten::add(%a.1, %b.1, %6)
    %3 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) = aten::add(%7, %c.1, %6)
    return (%3)
          """
      graph = torch._C.parse_ir(graph_str)
  
      kernel = te.TensorExprKernel(graph)
      res1 = kernel.run((x, y, z))
      res2 = kernel.fallback((x, y, z))
      correct = f(x, y, z)
      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 7 for calling the TestTensorExprPyBind obj's underlying member method code with example usage: obj.test_kernel_with_tensor_inputs() and returns: unknown