Quantization tensorflow package, explaination of addition parameters in CNN kernel (27 for 12 3X3 kernels, and 5 for dense layer)

Question

I am trying to understand the tensorflow quantization package but have some issues with the understanding of the number of nodes added after creating the quantization model for quantization-aware-training procedure

Quantization tensorflow package, explanation of addition parameters in CNN kernel (27 for 12 3X3 kernels, and 5 for dense layer) Model summary Without Quantization model:

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 reshape (Reshape)           (None, 28, 28, 1)         0         
                                                                 
 conv2d (Conv2D)             (None, 26, 26, 12)        120       
                                                                 
 max_pooling2d (MaxPooling2  (None, 13, 13, 12)        0         
 D)                                                              
                                                                 
 flatten (Flatten)           (None, 2028)              0         
                                                                 
 dense (Dense)               (None, 10)                20290     
                                                                 
=================================================================
Total params: 20410 (79.73 KB)
Trainable params: 20410 (79.73 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

Model summary After adding quantization nodes:

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 quantize_layer (QuantizeLa  (None, 28, 28)            3         
 yer)                                                            
                                                                 
 quant_reshape (QuantizeWra  (None, 28, 28, 1)         1         
 pperV2)                                                         
                                                                 
 quant_conv2d (QuantizeWrap  (None, 26, 26, 12)        147       
 perV2)                                                          
                                                                 
 quant_max_pooling2d (Quant  (None, 13, 13, 12)        1         
 izeWrapperV2)                                                   
                                                                 
 quant_flatten (QuantizeWra  (None, 2028)              1         
 pperV2)                                                         
                                                                 
 quant_dense (QuantizeWrapp  (None, 10)                20295     
 erV2)                                                           
                                                                 
=================================================================
Total params: 20448 (79.88 KB)
Trainable params: 20410 (79.73 KB)
Non-trainable params: 38 (152.00 Byte)
_________________________________________________________________

The number of differences in the parameters is not understandable.

I tried reading blog posts regarding this and what i understood is min/max and zeros of the equation needs to be found for the layers, which account for 3 constants but here the number of quantization variables changes as the type of the layer changes, so it is confusing.