✨[Feature] Support QAT export in dynamo

[peri044]

Is your feature request related to a problem? Please describe.
Tried nvidia-ammo 0.5 release.
Tried to torch.compile/torch.export the model with QAT nodes but faced the following error

assert isinstance(
AssertionError: expected FunctionType found method <bound method QuantLinearConvBase.quantized_forward of <class 'ammo.torch.quantization.nn.modules.quant_conv.QuantConv2d'>>

** To reproduce **

import torch
import ammo.torch.quantization as atq
from vgg16 import vgg16
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch_tensorrt

# Select quantization config
config = atq.INT8_DEFAULT_CFG

model = vgg16(num_classes=10, init_weights=False).eval().cuda()
calib_set = datasets.CIFAR10(
    root="./data",
    train=False,
    download=True,
    transform=transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ]
    ),
)

# Define forward loop for calibration
def forward_loop():
    for data in calib_set:
        model(data[0].unsqueeze(0).cuda())

# QAT with in-place replacement to quantized modules
atq.quantize(model, config, forward_loop)

x = torch.randn((1, 3, 32, 32), dtype=torch.float32).cuda()
# exp_program = torch.export.export(model, (x,))

compile_spec = {"inputs": [x,],
                "debug": True,
                "min_block_size": 1}
# Torch compile path
trt_model = torch.compile(model, backend="tensorrt", dynamic=None, options=compile_spec)

Describe alternatives you've considered

Additional context

[peri044]

Related issue open on Pytorch : pytorch/pytorch#114635