`sliding_window_inference` cannot handle output in formats other than tensor

[KumoLiu]

Describe the bug
sliding_window_inference cannot handle output in formats other than tensor which was a bug introduced by #6254.

MONAI/monai/inferers/utils.py

Line 312 in e4b313d

final_output = MetaTensor(final_output).copy_meta_from(temp_meta)

A clear and concise description of what the bug is.

RuntimeError                              Traceback (most recent call last)
Cell In [1], line 15
      6 model = HoVerNet(
      7     in_channels=3,
      8     out_classes=5,
      9     mode = "fast",
     10     adapt_standard_resnet=True
     11 )
     13 data = MetaTensor(torch.rand([2, 3, 512, 512]))
---> 15 sliding_window_inference(inputs=data, roi_size=(256, 256), sw_batch_size=2, predictor=model)

File /workspace/Code/MONAI/monai/inferers/utils.py:311, in sliding_window_inference(inputs, roi_size, sw_batch_size, predictor, overlap, mode, sigma_scale, padding_mode, cval, sw_device, device, progress, roi_weight_map, process_fn, buffer_steps, buffer_dim, *args, **kwargs)
    309 final_output = convert_to_dst_type(final_output, inputs, device=device)[0]  # type: ignore
    310 if temp_meta is not None:
--> 311     final_output = MetaTensor(final_output).copy_meta_from(temp_meta)
    312 return final_output

File /workspace/Code/MONAI/monai/data/meta_tensor.py:116, in MetaTensor.__new__(cls, x, affine, meta, applied_operations, *args, **kwargs)
    105 @staticmethod
    106 def __new__(
    107     cls,
   (...)
    113     **kwargs,
    114 ) -> MetaTensor:
    115     _kwargs = {"device": kwargs.pop("device", None), "dtype": kwargs.pop("dtype", None)} if kwargs else {}
--> 116     return torch.as_tensor(x, *args, **_kwargs).as_subclass(cls)

RuntimeError: Could not infer dtype of dict

To Reproduce

import torch
from monai.data import MetaTensor
from monai.networks.nets import HoVerNet
from monai.inferers.utils import sliding_window_inference

model = HoVerNet(
    in_channels=3,
    out_classes=5,
    mode = "fast",
    adapt_standard_resnet=True
)

data = MetaTensor(torch.rand([2, 3, 512, 512]))

sliding_window_inference(inputs=data, roi_size=(256, 256), sw_batch_size=2, predictor=model)

Expected behavior
sliding_window_inference shouldn't only handle tensor.

Environment

Ensuring you use the relevant python executable, please paste the output of:

================================
Printing MONAI config...
================================
MONAI version: 1.2.0rc2+34.gf98f0fda
Numpy version: 1.22.2
Pytorch version: 1.13.0a0+d0d6b1f
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: f98f0fda3fc57293917ba9a5cd7838bf05477191
MONAI __file__: /workspace/Code/MONAI/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.10
ITK version: 5.3.0
Nibabel version: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.1
gdown version: 4.6.0
TorchVision version: 0.14.0a0
tqdm version: 4.64.1
lmdb version: 1.3.0
psutil version: 5.9.2
pandas version: 1.4.4
einops version: 0.6.0
transformers version: 4.21.3
mlflow version: 2.0.1
pynrrd version: 1.0.0

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies


================================
Printing system config...
================================
System: Linux
Linux version: Ubuntu 20.04.5 LTS
Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.10
Processor: x86_64
Machine: x86_64
Python version: 3.8.13
Process name: python
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: []
Num physical CPUs: 12
Num logical CPUs: 20
Num usable CPUs: 20
CPU usage (%): [5.2, 4.6, 5.7, 4.6, 5.2, 4.0, 5.2, 4.6, 6.9, 4.6, 98.8, 4.6, 6.4, 4.6, 6.8, 5.1, 4.6, 4.6, 4.0, 4.6]
CPU freq. (MHz): 3621
Load avg. in last 1, 5, 15 mins (%): [1.4, 0.9, 0.9]
Disk usage (%): 81.9
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 62.6
Available memory (GB): 13.7
Used memory (GB): 48.2

================================
Printing GPU config...
================================
Num GPUs: 2
Has CUDA: True
CUDA version: 11.8
cuDNN enabled: True
cuDNN version: 8600
Current device: 0
Library compiled for CUDA architectures: ['sm_52', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'sm_90', 'compute_90']
GPU 0 Name: NVIDIA GeForce RTX 3090 Ti
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 84
GPU 0 Total memory (GB): 23.7
GPU 0 CUDA capability (maj.min): 8.6
GPU 1 Name: NVIDIA GeForce RTX 3090 Ti
GPU 1 Is integrated: False
GPU 1 Is multi GPU board: False
GPU 1 Multi processor count: 84
GPU 1 Total memory (GB): 23.7
GPU 1 CUDA capability (maj.min): 8.6

Additional context
May need @wyli to confirm.
Maybe a blocking issue for pathology_nuclei_segmentation_classification bundle.