parameters: (sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8')
return_statement: return Symbol(out)
docstring: 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.
docstring_summary: Given a symbol object representing a neural network of data type FP32,
      quantize it into a INT8 network.
docstring_tokens: Given a symbol object representing a neural network of data type FP32 quantize it into a INT8 network .
function: 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)
function_tokens: def _quantize_symbol ( sym , excluded_symbols = None , offline_params = None , quantized_dtype = 'int8' ) : 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 )