Is there a way to convert Proxy object to its original object

quantization
1

import torch.nn as nn
import copy
from torch.quantization.quantize_fx import prepare_fx, convert_fx
from torch.quantization import (
get_default_qconfig,
)

class MyModel(nn.Module):
def init(self):
super().init()
self.l1 = nn.Linear(4, 4)

def forward(self, x):
    l = int(x.shape[0] / 2)   
    return self.l1(x.view(l, -1))

model = MyModel()
model.eval()
qconfig = get_default_qconfig(“fbgemm”)
qconfig_dict = {
“”: qconfig,
# ‘object_type’: []
}
model_to_quantize = copy.deepcopy(model)
prepared_model = prepare_fx(model_to_quantize, qconfig_dict)
print("prepared model: ", prepared_model)

quantized_model = convert_fx(prepared_model)

File “/mnt/e/mm/FoundationModel-Agriculture/classification/mini_swin.py”, line 19, in forward
l = int(x.shape[0] / 2)
TypeError: int() argument must be a string, a bytes-like object or a number, not ‘Proxy’