Keras Input and Output layer name inconsistency with Keras' sequential model

Hi, I was working with sequential models using Keras 3, and I noticed that the output layer name of the current layer is different from the input layer name of the next layer. Let me explain in detail:

I have a sequential model which uses 2 Dense layer. When I printed the input and output name of each of those layers I noticed this:

Model definition:

import keras
import numpy as np

x_train = np.random.rand(32, 10)
y_train = np.random.rand(32, 10)

model = models.Sequential([
    layers.Input(shape=(10,)),
    layers.Dense(20, activation='relu'),
    layers.Dense(10, activation='tanh')
])

model.compile(
    optimizer='adam',
    loss='mean_squared_error',
    metrics=['mae']
)

model.fit(x_train, y_train, epochs=1)
model.save('Dense_model.h5')

Code to print input and output layer names

model = models.load_model('Dense_model.h5')
for layer in model.layers:
    print(layer.input.name)
    print(layer.output.name)
    print()

Output:

Shouldn't the keras_tensor_47 and keras_tensor_48 have the same name?

If the names are not supposed to be the same, then how Keras handles the layer sequence internally? I also noticed that this particular scenario seems to be occurring only in when I load this model from my disk. Whenever I train it and then directly use it as it (basically from memory) this doesn't happen

And this doesn't happen with Keras 2.x

Package information Keras: 3.11.1 Tensorflow: 2.17.0 Numpy: 1.26.4