I want to do Quantization Aware Training,

Here’s my model architecture.

Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
masking_4 (Masking)          (None, 389, 64)           0         
_________________________________________________________________
my_layer_5_4 (my_layer_5)       (None, 389, 512)          12288     
_________________________________________________________________
time_distributed_4 (TimeDist (None, 389, 39)           20007     
=================================================================

I follow the tfmot.quantization.keras.QuantizeConfig,
I want all of them to use quantization.
Here’s my code

import tensorflow_model_optimization as tfmot
from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_configs
NoOpQuantizeConfig = default_8bit_quantize_configs.NoOpQuantizeConfig

class NoOpQuantizeConfig(tfmot.quantization.keras.QuantizeConfig):
    """QuantizeConfig which does not quantize any part of the layer."""
    def get_weights_and_quantizers(self, layer):
        return []
    def get_activations_and_quantizers(self, layer):
        return []
    def set_quantize_weights(self, layer, quantize_weights):
        pass
    def set_quantize_activations(self, layer, quantize_activations):
        pass
    def get_output_quantizers(self, layer):
        return []
    def get_config(self):
        return {}
  
def apply_quantization(layer):
    if isinstance(layer, (tf.keras.layers.TimeDistributed, tf.keras.layers.Masking,tf.keras.layers.my_layer_5_4  )):
        return tfmot.quantization.keras.quantize_annotate_layer(layer, quantize_config=NoOpQuantizeConfig())
    else:
        return tfmot.quantization.keras.quantize_annotate_layer(layer)

if __name__ == '__main__':
    model = load_model('./model.h5',custom_objects = {'my_layer_5': my_layer_5})
    model.summary()

    annotated_model = tf.keras.models.clone_model(
        model,
        clone_function=apply_quantization,
    )

    with tf.keras.utils.custom_object_scope({"NoOpQuantizeConfig": NoOpQuantizeConfig}):
        q_aware_model = tfmot.quantization.keras.quantize_apply(annotated_model)
    
    q_aware_model.summary()

When I run it, but shows a error

ValueError: ('Expected `model` argument to be a `Model` instance, got ', <keras.engine.sequential.Sequential object at 0x7f234263dfd0>)```