How to quantize ViT model with quantization aware training

It can train the ViT model from the Hugging Face transformer,
but when converting to tflite model it appear an error message that I can't solve it.
The following are the tinynn setting and the error message

Transformers version is 4.26.0
```
from tinynn.graph.tracer import import_patcher
from tinynn.converter import TFLiteConverter
from tinynn.graph.quantization.quantizer import QATQuantizer

with import_patcher():
from transformers import ViTForImageClassification

device = 'cuda'

transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

class ViTWrapper(nn.Module):
def __init__(self, vit):
super().__init__()
self.vit = vit

def forward(self, x):
return self.vit(x).logits

model_url = 'google/vit-base-patch16-224'

with import_patcher():
model = ViTForImageClassification.from_pretrained(model_url)
Vit = ViTWrapper(model)

################ QAT SET #######################
dummy = torch.rand([1,3,224,224])
mean = (0.485 + 0.456 + 0.406) / 3 * 255
std = (0.229 + 0.224 + 0.225) / 3 * 255
config={'asymmetric': True, 'per_tensor': False, 'backend':'fbgemm',"rewrite_graph": False,'quantized_input_stats':[(float(mean), float(std))]}
quantizer = QATQuantizer(Vit, dummy, work_dir='out',config=config)
Vit = quantizer.quantize()
Vit.qconfig = torch.quantization.get_default_qat_qconfig("fbgemm")
Vit = torch.quantization.prepare_qat(Vit)
Vit.cuda()

#####Skip training Step#####

##### Convert tflite Step#####
if((epoch) % 1 == 0):
with torch.no_grad():
device = 'cpu'
Vit.eval()
Vit.to(device)
Vit = quantizer.convert(Vit)
torch.backends.quantized.engine = quantizer.backend
converter = TFLiteConverter(Vit, dummy, tflite_path='./qat_model.tflite',fuse_quant_dequant=True)
converter.convert()

```

The error message:

```
Traceback (most recent call last):
File "c:\Users\user\Desktop\ViT\tinynn_PTQ\QAT.py", line 100, in
converter.convert()
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\tinynn\converter\base.py", line 512, in convert
self.init_jit_graph()
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\tinynn\converter\base.py", line 237, in init_jit_graph
script = torch.jit.trace(self.model, self.dummy_input)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\jit\_trace.py", line 1000, in trace
traced_func = _trace_impl(
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\jit\_trace.py", line 695, in _trace_impl
return trace_module(
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\jit\_trace.py", line 1275, in trace_module
module._c._create_method_from_trace(
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1543, in _slow_forward
result = self.forward(*input, **kwargs)
File "c:\Users\user\Desktop\ViT\tinynn_PTQ\QAT.py", line 39, in forward
return self.vit(x).logits
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1543, in _slow_forward
result = self.forward(*input, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\transformers\models\vit\modeling_vit.py", line 789, in forward
outputs = self.vit(
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\torch\nn\modules\module.py", line 1543, in _slow_forward
result = self.forward(*input, **kwargs)
File "C:\Users\user\anaconda3\envs\tf1\lib\site-packages\transformers\models\vit\modeling_vit.py", line 571, in forward
expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
AttributeError: 'function' object has no attribute 'dtype
```

Start with the user’s QAT.py around line 100 and the converter entry points named in the traceback, especially converter/base.py:init_jit_graph. Reproduce the trace using Transformers 4.26.0 and the shown ViT/QAT setup, then determine the compatibility issue and verify that converter.convert() completes without the reported AttributeError.

Written by the indexing model from the issue text.