🐛 [Bug] Encountered bug when using Torch-TensorRT(torch.nn.LSTM)

Bug Description

Encountered error as follow when using Torch-TensorRT to convert torch.nn.LSTM in docker image nvcr.io/nvidia/pytorch:23.12-py3 :
NotImplementedError: aten::_cudnn_rnn_flatten_weight: attempted to run this operator with Meta tensors, but there was no abstract impl or Meta kernel registered. You may have run into this message while using an operator with PT2 compilation APIs (torch.compile/torch.export); in order to use this operator with those APIs you'll need to add an abstract impl

To Reproduce

example code:

import torch
import torch_tensorrt
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(1024, 1024, batch_first=True)

    def forward(self, x):
        x = self.lstm(x)[0]
        return x

model = Model().half().eval().cuda()
inputs = [torch.randn(100, 200, 1024).half().cuda()]
trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
trt_gm(*inputs)

Expected behavior

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version: 2.2.0a0
• PyTorch Version: 2.2.0a0+81ea7a4
• CPU Architecture:
• OS (e.g., Linux): Linux
• How you installed PyTorch (conda, pip, libtorch, source):
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version: 3.10.12
• CUDA version: 12.3
• GPU models and configuration: A100
• Any other relevant information:

Additional context