大佬求帮助，在训练atss检测的时候，按照官方dump成mge出错，.tm模型不能可视化

## 环境
1.系统环境：Ubuntu 22.04.2
2.MegEngine版本：1.13.0
3.python版本：3.10.12
4.模型名称：atss_res18_coco_3x_800size

## 复现步骤
1.训练
```
python official/vision/detection/tools/train.py -f official/vision/detection/configs/atss_res18_coco_3x_800size.py -n 1 -d data/coco/
```
2.convert
```
python convert.py -f official/vision/detection/configs/atss_res18_coco_3x_800size.py -w log-of-atss_res18_coco_3x_800size/epoch_9.pkl -i official/assets/cat.jpg
```
3.

## 请提供关键的代码片段便于追查问题
```
**##转换代码**
import numpy as np
import megengine.functional as F
import megengine.hub
from megengine import jit, tensor
import megengine as mge
import megengine.distributed as dist
from megengine.autodiff import GradManager
from megengine.data import DataLoader, Infinite, RandomSampler
from megengine.data import transform as T
from megengine.optimizer import SGD

from official.vision.detection.tools.data_mapper import data_mapper
from official.vision.detection.tools.utils import DetEvaluator, import_from_file
import megengine.traced_module as tm
import argparse
import bisect
import copy
import os
import time
import cv2

def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument("-i", "--image", type=str)
return parser

if __name__ == "__main__":
parser = make_parser()
args = parser.parse_args()

current_network = import_from_file(args.file)
cfg = current_network.Cfg()
cfg.backbone_pretrained = False
model = current_network.Net(cfg)

ori_img = cv2.imread(args.image)
image, im_info = DetEvaluator.process_inputs(
ori_img.copy(), model.cfg.test_image_short_size, model.cfg.test_image_max_size,
)
state_dict = mge.load(args.weight_file)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
model.eval()

traced_resnet = tm.trace_module(model, mge.tensor(image),im_info=mge.tensor(im_info))
# 可以在这里进行基于 trace_module 的图手术，以及模型转换
traced_resnet.eval()
mge.save(traced_resnet,"test.tm")
@jit.trace(symbolic=True, capture_as_const=True)
def infer_func(data, im_info, model):
pred = model(data,im_info)
return pred

output = infer_func(mge.tensor(image),im_info=mge.tensor(im_info), model=traced_resnet)
infer_func.dump("log-of-atss_res18_coco_3x_800size/test.mge", arg_names=["data"])
```

## 请提供完整的日志及报错信息
```
25 17:37:50[mgb] WRN [dnn]
Cudnn8 will jit ptx code with cache. You can set
CUDA_CACHE_MAXSIZE and CUDA_CACHE_PATH environment var to avoid repeat jit(very slow).
For example `export CUDA_CACHE_MAXSIZE=2147483647` and `export CUDA_CACHE_PATH=/data/.cuda_cache`
25 17:37:53[mgb] ERR error while applying optimization pass PassConvertToCompatible: bad input shape for polyadic operator: {256}, {1,256,136,100}

backtrace:
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb13MegBrainErrorC1ERKSs+0x4a) [0x7fdfdad5590a]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x2db7867) [0x7fdfdadb7867]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN6megdnn12ErrorHandler15on_megdnn_errorERKSs+0x14) [0x7fdfde9d86a4]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x6a078d8) [0x7fdfdea078d8]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x6a07a91) [0x7fdfdea07a91]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb3opr8Elemwise20get_output_var_shapeEN6megdnn5param8Elemwise4ModeERKNS2_11SmallVectorINS2_11TensorShapeELj4EEE+0x37) [0x7fdfdaf150e7]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZNK3mgb3opr8Elemwise20get_output_var_shapeERKN6megdnn11SmallVectorINS2_11TensorShapeELj4EEERS5_+0x29) [0x7fdfdaf1e0c9]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb2cg5mixin24OutshapePureByInshapeOpr10infer_descEmRN6megdnn11TensorShapeERKNS0_12static_infer6InpValE+0x19d) [0x7fdfdade9e0d]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb2cg12static_infer22StaticInferManagerImpl13TagShapeTrait8do_inferERKNS1_6InpValE+0x57) [0x7fdfdae0b437]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x2e0b292) [0x7fdfdae0b292]

Traceback (most recent call last):
File "/home/csy/megvii/Models/convert.py", line 63, in
infer_func.dump("log-of-atss_res18_coco_3x_800size/test.mge", arg_names=["data"])
File "/home/csy/.local/lib/python3.10/site-packages/megengine/jit/tracing.py", line 1183, in dump
dump_content, dump_info = G.dump_graph(
File "/home/csy/.local/lib/python3.10/site-packages/megengine/core/tensor/megbrain_graph.py", line 456, in dump_graph
dump_content = _imperative_rt.dump_graph(
RuntimeError: bad input shape for polyadic operator: {256}, {1,256,136,100}

backtrace:
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb13MegBrainErrorC1ERKSs+0x4a) [0x7fdfdad5590a]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x2db7867) [0x7fdfdadb7867]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN6megdnn12ErrorHandler15on_megdnn_errorERKSs+0x14) [0x7fdfde9d86a4]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x6a078d8) [0x7fdfdea078d8]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x6a07a91) [0x7fdfdea07a91]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb3opr8Elemwise20get_output_var_shapeEN6megdnn5param8Elemwise4ModeERKNS2_11SmallVectorINS2_11TensorShapeELj4EEE+0x37) [0x7fdfdaf150e7]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZNK3mgb3opr8Elemwise20get_output_var_shapeERKN6megdnn11SmallVectorINS2_11TensorShapeELj4EEERS5_+0x29) [0x7fdfdaf1e0c9]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb2cg5mixin24OutshapePureByInshapeOpr10infer_descEmRN6megdnn11TensorShapeERKNS0_12static_infer6InpValE+0x19d) [0x7fdfdade9e0d]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb2cg12static_infer22StaticInferManagerImpl13TagShapeTrait8do_inferERKNS1_6InpValE+0x57) [0x7fdfdae0b437]
/home/csy/.local/lib/python3.10/site-packages/megengine/core/lib/libmegengine_shared.so(+0x2e0b292) [0x7fdfdae0b292]

```

Start with convert.py and the ATSS configuration at official/vision/detection/configs/atss_res18_coco_3x_800size.py, following the trace_module and infer_func.dump calls. Reproduce the conversion with the listed training weights and image, then investigate the reported shape mismatch and verify that the resulting model dump can be visualized.

Written by the indexing model from the issue text.