parameters: (value: int, scale: float, offset: float, from_dtype: str)
return_statement: return __DTYPE_TO_DEQUANTIZE_FUNCTION[from_dtype][1](int(value), scale, offset)
docstring: Dequantize the given value from the given datatype using Arm NN.
  
      This function can be used to convert an 8-bit unsigned integer value or 8/16/32-bit signed
      integer value into a 32-bit floating point value. Typically used when decoding an
      output value from an output tensor on a quantized model.
  
      Args:
          value (int): The value to be dequantized. Value could be numpy numeric data type.
          scale (float): A numeric constant that the value is multiplied by.
          offset (float): A 'zero-point' used to 'shift' the integer range.
          from_dtype (str): The data type 'value' represents. Supported values: 'unit8', 'int8', 'int16', 'int32'.
  
      Returns:
          float: A dequantized 32-bit floating-point value.
docstring_summary: Dequantize the given value from the given datatype using Arm NN.
docstring_tokens: Dequantize the given value from the given datatype using Arm NN .
function: def dequantize(value: int, scale: float, offset: float, from_dtype: str) -> float:
      """Dequantize the given value from the given datatype using Arm NN.
  
      This function can be used to convert an 8-bit unsigned integer value or 8/16/32-bit signed
      integer value into a 32-bit floating point value. Typically used when decoding an
      output value from an output tensor on a quantized model.
  
      Args:
          value (int): The value to be dequantized. Value could be numpy numeric data type.
          scale (float): A numeric constant that the value is multiplied by.
          offset (float): A 'zero-point' used to 'shift' the integer range.
          from_dtype (str): The data type 'value' represents. Supported values: 'unit8', 'int8', 'int16', 'int32'.
  
      Returns:
          float: A dequantized 32-bit floating-point value.
      """
  
      # specifies which function to use with given datatype and the value range for that data type.
      if from_dtype not in __DTYPE_TO_DEQUANTIZE_FUNCTION:
          raise ValueError("""Unexpected value datatype {} given.
                           Armnn currently supports dequantization from {} values.""".format(from_dtype, list(__DTYPE_TO_DEQUANTIZE_FUNCTION.keys())))
  
      input_range = __DTYPE_TO_DEQUANTIZE_FUNCTION[from_dtype][0]
  
      if not input_range[0] <= value <= input_range[1]:
          raise ValueError('Value is not within range of the given datatype {}'.format(from_dtype))
  
      return __DTYPE_TO_DEQUANTIZE_FUNCTION[from_dtype][1](int(value), scale, offset)
function_tokens: def dequantize ( value : int , scale : float , offset : float , from_dtype : str ) -> float : # specifies which function to use with given datatype and the value range for that data type. if from_dtype not in __DTYPE_TO_DEQUANTIZE_FUNCTION : raise ValueError ( """Unexpected value datatype {} given.
                           Armnn currently supports dequantization from {} values.""" . format ( from_dtype , list ( __DTYPE_TO_DEQUANTIZE_FUNCTION . keys ( ) ) ) ) input_range = __DTYPE_TO_DEQUANTIZE_FUNCTION [ from_dtype ] [ 0 ] if not input_range [ 0 ] <= value <= input_range [ 1 ] : raise ValueError ( 'Value is not within range of the given datatype {}' . format ( from_dtype ) ) return __DTYPE_TO_DEQUANTIZE_FUNCTION [ from_dtype ] [ 1 ] ( int ( value ) , scale , offset )