docstr: 测试张量转换后的反向传播是否正确
docstr_tok: ["\u6d4b\u8bd5\u5f20\u91cf\u8f6c\u6362\u540e\u7684\u53cd\u5411\u4f20\u64ad\u662f\u5426\u6b63\u786e"]
code: def test_gradient(self):
      """
      测试张量转换后的反向传播是否正确
      """
  
      x = paddle.to_tensor(
          [1.0, 2.0, 3.0, 4.0, 5.0], stop_gradient=False
      )
      y = paddle2torch(x)
      z = 3 * (y**2)
      z.sum().backward()
      assert y.grad.tolist() == [6, 12, 18, 24, 30]
  
imports: import pytest
  from fastNLP.envs.imports import _NEED_IMPORT_JITTOR, _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
  from fastNLP.modules.mix_modules.utils import paddle2torch, torch2paddle, jittor2torch, torch2jittor
label_desc: Use image node_id 2 for calling the TestPaddle2Torch obj's underlying member method code with example usage: obj.test_gradient() without return types