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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
 """Given a symbol object representing a neural network of data type FP32, quantize it into a INT8 network. Parameters sym : Symbol FP32 neural network symbol. excluded_sym_names : list of strings A list of strings representing the names of the symbols that users want to excluding from being quantized. offline_params : list of strs Names of the parameters that users want to quantize offline. It's always recommended to quantize parameters offline so that quantizing parameters during the inference can be avoided. quantized_dtype: str The quantized destination type for input data. """

    num_excluded_symbols = 0
    if excluded_symbols is not None:
        assert isinstance(excluded_symbols, list)
        num_excluded_symbols = len(excluded_symbols)
    else:
        excluded_symbols = []

    num_offline = 0
    offline = []
    if offline_params is not None:
        num_offline = len(offline_params)
        for k in offline_params:
            offline.append(c_str(k))

    out = SymbolHandle()
    check_call(_LIB.MXQuantizeSymbol(sym.handle,
                                     ctypes.byref(out),
                                     mx_uint(num_excluded_symbols),
                                     c_str_array(excluded_symbols),
                                     mx_uint(num_offline),
                                     c_array(ctypes.c_char_p, offline),
                                     c_str(quantized_dtype),
                                     ctypes.c_bool(True)))
    return Symbol(out)