trt-engine-explorer failure of TensorRT 8.6 when running EnginePlan(f'{PATH}/graph.json', f'{PATH}/profile.json')

Description

what's the "LayerType": "TrainStation" is ?

when i use the [trt-engine-explorer], I get the following error:

plan = EnginePlan(f'{PATH}/graph.json', f'{PATH}/profile.json')
Traceback (most recent call last):
File "", line 1, in
File "/home/disk_1/zyq/work/TensorRT/tools/experimental/trt-engine-explorer/trex/engine_plan.py", line 127, in init
graph_df = construct_df(raw_layers)
File "/home/disk_1/zyq/work/TensorRT/tools/experimental/trt-engine-explorer/trex/engine_plan.py", line 106, in construct_df
graph_df = fix_df(graph_df)
File "/home/disk_1/zyq/work/TensorRT/tools/experimental/trt-engine-explorer/trex/df_preprocessing.py", line 176, in fix_df
__fix_output_precision(df)
File "/home/disk_1/zyq/work/TensorRT/tools/experimental/trt-engine-explorer/trex/df_preprocessing.py", line 164, in __fix_output_precision
df['output_precision'] = [Activation(outputs[1]).precision for outputs in df['Outputs']]
File "/home/disk_1/zyq/work/TensorRT/tools/experimental/trt-engine-explorer/trex/df_preprocessing.py", line 164, in
df['output_precision'] = [Activation(outputs[1]).precision for outputs in df['Outputs']]
IndexError: list index out of range

I think this may be caused by the TrainStation layer, since in my graph.json,there is a layer like this:

"Layers": [{
"Name": "[trainStation1]",
"LayerType": "TrainStation",
"Inputs": [],
"Outputs": [],
"TacticValue": "0x0000000000000000",
"StreamId": 0,
"Metadata": ""
}

Environment

TensorRT Version: 8.6

NVIDIA GPU: 3090

NVIDIA Driver Version: 525.105.17

CUDA Version: 11.6

CUDNN Version: 8.9.2.26_cuda11

Operating System: ubuntu 18.04

Python Version (if applicable): 3.9.18

Tensorflow Version (if applicable):

PyTorch Version (if applicable):

Baremetal or Container (if so, version):

Relevant Files

Model link:

Steps To Reproduce

Commands or scripts:

Have you tried the latest release?:

Can this model run on other frameworks? For example run ONNX model with ONNXRuntime (polygraphy run <model.onnx> --onnxrt):