parameters: (w_quantized, w_min, w_max)
return_statement: return (w_min +
            w_quantized.astype(np.float64) / np.power(2, bits) * (w_max - w_min))
docstring: Simulate weight de-quantization.
  
    Args:
      w: (a numpy.ndarray) The weight to be quantized.
      bits: (int) number of bits used for the quantization: 8 or 16.
  
    Returns:
      A tuple with three elements:
        w_quantized: the quantized version of w, represented as an uint8-
          or uint16-type numpy.ndarray.
        w_min: Minimum value of w, required for dequantization.
        w_max: Maximum value of w, required for dequantization.
docstring_summary: Simulate weight de-quantization.
docstring_tokens: Simulate weight de - quantization .
function: def dequantize(w_quantized, w_min, w_max):
    """
    Simulate weight de-quantization.
  
    Args:
      w: (a numpy.ndarray) The weight to be quantized.
      bits: (int) number of bits used for the quantization: 8 or 16.
  
    Returns:
      A tuple with three elements:
        w_quantized: the quantized version of w, represented as an uint8-
          or uint16-type numpy.ndarray.
        w_min: Minimum value of w, required for dequantization.
        w_max: Maximum value of w, required for dequantization.
    """
    if w_quantized.dtype == np.uint8:
      bits = 8
    elif w_quantized.dtype == np.uint16:
      bits = 16
    else:
      raise ValueError(
          'Unsupported dtype in quantized values: %s' % w_quantized.dtype)
    return (w_min +
            w_quantized.astype(np.float64) / np.power(2, bits) * (w_max - w_min))
function_tokens: def dequantize ( w_quantized , w_min , w_max ) : if w_quantized . dtype == np . uint8 : bits = 8 elif w_quantized . dtype == np . uint16 : bits = 16 else : raise ValueError ( 'Unsupported dtype in quantized values: %s' % w_quantized . dtype ) return ( w_min + w_quantized . astype ( np . float64 ) / np . power ( 2 , bits ) * ( w_max - w_min ) )