def METHOD_NAME(self):
    """ Verify deeplab_v3 """
    # Set the strict flag to False so that torch.jit.trace can be successful.
    from aimet_torch.meta import connectedgraph
    connectedgraph.jit_trace_args.update({"strict": False})
    if Version(torchvision.__version__) < Version('0.10.2'):
        model = models.segmentation.deeplabv3_resnet50(pretrained_backbone=False).eval().cuda()
    else:
        model = models.segmentation.deeplabv3_resnet50(weights_backbone=None).eval().cuda()
    prepared_model = prepare_model(model)
    print(prepared_model)
    input_shape = (1, 3, 224, 224)
    dummy_input = torch.randn(*input_shape).cuda()
    # Verify bit-exact outputs.
    assert torch.equal(prepared_model(dummy_input)['out'], model(dummy_input)['out'])
    # Verify that validator checks pass.
    assert ModelValidator.validate_model(prepared_model, dummy_input)
    # Verify with Quantization workflow.
    quant_sim = QuantizationSimModel(prepared_model, dummy_input=dummy_input)
    quant_sim.compute_encodings(evaluate, dummy_input)
    quant_sim.model(dummy_input)