The Code as Follow:
import torch
import torch.nn as nn
import torch.optim as optim

class mturn_predictor(nn.Module):
    def __init__(self, *args):
        super().__init__()
        self.QuantStub = torch.quantization.QuantStub()
        self.cls_score_parking_goal_combine_multi_layers_0_new = nn.Linear(in_features=16, out_features=1, bias=True)
        self.DeQuantStub = torch.quantization.DeQuantStub()

    def forward(self, x):
        Cat_out = self.QuantStub(x)
        cls_score_parking_goal_combine_multi_layers_0_new = self.cls_score_parking_goal_combine_multi_layers_0_new(Cat_out)
        DeQuantStub_out = self.DeQuantStub(cls_score_parking_goal_combine_multi_layers_0_new)
        return DeQuantStub_out

def generate_input():
    example = torch.tensor([[4.4249,0,0,0,2.5477,0,4.559,0,0,0,0.5364,3.3522,2.1454,0,0,0]])
    return example

if __name__ == "__main__":
    model = mturn_predictor()
    input_data = generate_input()

    print(input_data)
    output_data = model(input_data)

    ## convert into qat model
    model.train()
    model.qconfig = torch.quantization.get_default_qat_qconfig("qnnpack")
    torch.quantization.prepare_qat(model, inplace=True)

    output_data = model(input_data)

    model.eval()
    model_qat = torch.quantization.convert(model)
    print("torch.quantization.convert Done")

    weight_tensor = torch.tensor([[-0.5172,-0.6572,0.264,0.4471,0.2855,-0.5549,0.4741,0.4364,-0.5118,-0.0162,-0.3448,-0.5172,-0.6896,-0.3448,-0.6465,-0.4902]])
    weight_qat = torch.quantize_per_tensor(weight_tensor, 0.00538725778460502, 0, torch.qint8)

    bias_tensor = torch.tensor([-0.17078383266925812])
    model_qat.cls_score_parking_goal_combine_multi_layers_0_new._packed_params.set_weight_bias(weight_qat, bias_tensor)
    model_qat.cls_score_parking_goal_combine_multi_layers_0_new.scale = 0.10898102819919586
    model_qat.cls_score_parking_goal_combine_multi_layers_0_new.zero_point = 186

    model_qat.QuantStub.register_buffer('scale', torch.tensor([0.13408887386322021]))
    model_qat.QuantStub.register_buffer('zero_point', torch.tensor([173], dtype=torch.long))

    print(model_qat)
    #import pdb; pdb.set_trace()


    print("Input:", input_data)
    print("Weight:", model_qat.cls_score_parking_goal_combine_multi_layers_0_new._packed_params)
    output_data = model_qat(input_data)
    print("Output:", output_data)

    print("Matmul: ", torch.matmul(input_data, torch.transpose(weight_tensor, 0, 1)))

The result of Matmul and Linear is different,
Output: tensor([[7.4107]])
Matmul:  tensor([[-2.7979]])