I have a model with input layer as shape (None,128,128,1)
Model: “model”


Layer (type)                 Output Shape              Param #
input_1 (InputLayer)         [(None, 128, 128, 1)]     0

conv2d (Conv2D)              (None, 126, 126, 16)      160

max_pooling2d (MaxPooling2D) (None, 63, 63, 16)        0

conv2d_1 (Conv2D)            (None, 61, 61, 32)        4640

max_pooling2d_1 (MaxPooling2 (None, 30, 30, 32)        0

flatten (Flatten)            (None, 28800)             0

dense (Dense)                (None, 128)               3686528


dense_1 (Dense)              (None, 10)                1290
Total params: 3,692,618
Trainable params: 3,692,618
Non-trainable params: 0
now i have trained and saved the model and later loaded the model USING
new_model = tf.keras.models.load_model('saved_model/my_model')
``
Now i want to change the input layer with shape (256,256,1)
and use the rest of the model weights the same 
how can I do this?
What I have tried 
```python
new_model.layers.pop(0) 

mode=tf.keras.Sequential()
mode.add(tf.keras.Input(shape=(256,256,1)))
for lay in new_model.layers[1:]:
    mode.add(lay)

none of these work for me
is there a way?