def METHOD_NAME(self):
    # Test for per-channel Quant
    torch.manual_seed(67)
    my_model = ModelMultipleOps().to(torch.float32)
    my_model.eval()
    calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
    eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
    out_ref = my_model(eval_data)
    q_model = torch.ao.quantization.QuantWrapper(my_model)
    q_model.eval()
    q_model.qconfig = torch.ao.quantization.default_per_channel_qconfig
    torch.ao.quantization.fuse_modules(q_model.module, [['conv1', 'bn1', 'relu1']], inplace=True)
    torch.ao.quantization.prepare(q_model)
    q_model(calib_data)
    torch.ao.quantization.convert(q_model)
    out_q = q_model(eval_data)
    SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_q))
    # Quantized model output should be close to floating point model output numerically
    # Setting target SQNR to be 35 dB
    self.assertGreater(SQNRdB, 35, msg='Quantized model numerics diverge from float, expect SQNR > 35 dB')