`sliding_window_inference` shouldn't remove gradient

[wyli]

Describe the bug

import torch
import monai

inputs = torch.ones((3, 16, 15, 7), requires_grad=True)
result = monai.inferers.sliding_window_inference(inputs, roi_size=(2, 3), sw_batch_size=3, predictor=lambda x: x + 1.0)
result.sum().backward()
print(inputs.grad)

Traceback (most recent call last):
  File "my_test.py", line 8, in <module>
    result.sum().backward()
  File "/usr/local/anaconda3/envs/py38/lib/python3.8/site-packages/torch/_tensor.py", line 488, in backward
    torch.autograd.backward(
  File "/usr/local/anaconda3/envs/py38/lib/python3.8/site-packages/torch/autograd/__init__.py", line 197, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

the detach should be done before entering this sliding window function, the function shouldn't always remove the gradients:

MONAI/monai/inferers/utils.py

Line 265 in 88fb0b1

output_image_list[ss] = output_image_list[ss].detach()

Pytorch version: 1.13.1
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 88fb0b1

cc @dongyang0122