How to use the nncf.quantization.SymmetricQuantizer function in nncf

print()
    print("CUDA DataParallel high batch")
    print("------------------------------------------------")
    print("Pytorch Symmetric(cuda {0}) DataParallel impl:".format(device_ids))
    print("input size: {0}".format(HIGH_BATCH_INPUT_SIZE))
    run_profile(
        nn.parallel.DataParallel(ReferenceQuantize(NBITS).cuda(), device_ids=device_ids),
        HIGH_BATCH_INPUT_SIZE,
        'cuda',
        GPU_RUNS_HIGH_BATCH)

    print()
    print("Custom Symmetric (cuda {0}) DataParallel impl:".format(device_ids))
    print("input size: {0}".format(HIGH_BATCH_INPUT_SIZE))
    run_profile(
        nn.parallel.DataParallel(SymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS))).cuda(),
                                 device_ids=device_ids),
        HIGH_BATCH_INPUT_SIZE,
        'cuda',
        GPU_RUNS_HIGH_BATCH)

    print()
    print("Custom Asymmetric (cuda {0}) DataParallel impl:".format(device_ids))
    print("input size: {0}".format(HIGH_BATCH_INPUT_SIZE))
    run_profile(
        nn.parallel.DataParallel(AsymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS))).cuda(),
                                 device_ids=device_ids),
        HIGH_BATCH_INPUT_SIZE,
        'cuda',
        GPU_RUNS_HIGH_BATCH)

    # CUDA DataParallel high batch

print("CUDA DistributedDataParallel high batch")
    print("------------------------------------------------")
    print("Pytorch Symmetric(cuda {0}) DistributedDataParallel impl:".format(device_ids))
    print("input size: {0}".format(HIGH_BATCH_INPUT_SIZE))
    mp.spawn(
        run_worker,
        nprocs=NGPUS_PER_NODE,
        args=(WORLD_SIZE, ReferenceQuantize(NBITS), TEST_PARAMS_STRUCT[1], GPU_RUNS_HIGH_BATCH))

    print()
    print("Custom Symmetric (cuda {0}) DistributedDataParallel impl:".format(device_ids))
    print("input size: {0}".format(HIGH_BATCH_INPUT_SIZE))
    mp.spawn(
        run_worker,
        nprocs=NGPUS_PER_NODE,
        args=(WORLD_SIZE, SymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS))), TEST_PARAMS_STRUCT[1],
              GPU_RUNS_HIGH_BATCH))

    print()
    print("Custom Asymmetric (cuda {0}) DistributedDataParallel impl:".format(device_ids))
    print("input size: {0}".format(HIGH_BATCH_INPUT_SIZE))
    mp.spawn(
        run_worker,
        nprocs=NGPUS_PER_NODE,
        args=(WORLD_SIZE, SymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS))), TEST_PARAMS_STRUCT[1],
              GPU_RUNS_HIGH_BATCH))

print("Pytorch Symmetric Per Weight Channel (cpu) impl:")
    print("input size: {0}".format(LOW_BATCH_INPUT_SIZE))
    run_profile(
        ReferenceQuantize(NBITS,
                          input_shape=LOW_BATCH_INPUT_SIZE,
                          per_channel=True,
                          is_weights=True),
        LOW_BATCH_INPUT_SIZE,
        'cpu',
        CPU_RUNS)

    print()
    print("Custom Symmetric Per Weight Channel  (cpu) impl")
    print("input size: {0}".format(LOW_BATCH_INPUT_SIZE))
    run_profile(
        SymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS),
                                           input_shape=LOW_BATCH_INPUT_SIZE,
                                           per_channel=True,
                                           is_weights=True)),
        LOW_BATCH_INPUT_SIZE,
        'cpu',
        CPU_RUNS)

    print()
    print("Pytorch Symmetric Per Activation Channel (cpu) impl:")
    print("input size: {0}".format(LOW_BATCH_INPUT_SIZE))
    run_profile(
        ReferenceQuantize(NBITS,
                          input_shape=LOW_BATCH_INPUT_SIZE,
                          per_channel=True,
                          is_weights=False),
        LOW_BATCH_INPUT_SIZE,