def apply(self, model):
    '\n        Quantize the weights and activations to calculate for specific \n        layers.\n\n        Args:\n            model(paddle.nn.Layer): The target model which would\n                calculate the input quantization scale.\n\n        Returns:\n            None\n        '
    assert isinstance(model, dygraph.Layer), 'The model must be the instance of dygraph.Layer.'
    for (name, cur_layer) in model.named_sublayers():
        if ((not isinstance(cur_layer, self._quantizable_layer_type)) or (hasattr(cur_layer, 'skip_quant') and (cur_layer.skip_quant == True))):
            continue
        (parent_layer, sub_name) = utils.find_parent_layer_and_sub_name(model, name)
        cur_quant_layer = self._get_input_quantized_layer(cur_layer)
        setattr(parent_layer, sub_name, cur_quant_layer)
Quantize the weights and activations to calculate for specific 
layers.

Args:
    model(paddle.nn.Layer): The target model which would
        calculate the input quantization scale.

Returns:
    None
def apply(self, model):
    '\n        Quantize the weights and activations to calculate for specific \n        layers.\n\n        Args:\n            model(paddle.nn.Layer): The target model which would\n                calculate the input quantization scale.\n\n        Returns:\n            None\n        '
    assert isinstance(model, dygraph.Layer), 'The model must be the instance of dygraph.Layer.'
    for (name, cur_layer) in model.named_sublayers():
        if ((not isinstance(cur_layer, self._quantizable_layer_type)) or (hasattr(cur_layer, 'skip_quant') and (cur_layer.skip_quant == True))):
            continue
        (parent_layer, sub_name) = utils.find_parent_layer_and_sub_name(model, name)
        cur_quant_layer = self._get_input_quantized_layer(cur_layer)
        setattr(parent_layer, sub_name, cur_quant_layer)