keras.Model.save changes binary every time model saved

Question

Why does keras.Model.save() produce different binaries with every run, when, AFAIU, I have taken all the necessary steps for complete reproducibility of the results and even binaries?

You can verify this by simply executing the following script within a docker container. The saved_model.pb and contents of the variables directory always change. Create a container with:
docker run --rm --volume=/host/dir/with/script:/workspace -it tensorflow/tensorflow:2.4.1 bash

# Setup environmental variables.
import os
os.environ["TF_DETERMINISTIC_OPS"] = "1"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import numpy as np
import random
import tensorflow as tf
import time


def initialise_seeds(seed):
    os.environ["PYTHONHASHSEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    tf.random.set_seed(seed)


SEED = 42
initialise_seeds(SEED)

if __name__ == '__main__':
    mnist = tf.keras.datasets.mnist

    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0

    model = tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10),
        tf.keras.layers.Softmax()
    ], name="best_ever")

    model.compile(
        optimizer='adam',
        loss=tf.keras.losses.SparseCategoricalCrossentropy(),
        metrics=['accuracy']
    )
    model.fit(x_train, y_train, epochs=2)
    model.evaluate(x_test,  y_test, verbose=2)

    # Save the model weights.
    ckpt = "/cp-0.ckpt"
    model.save_weights(model.name + ckpt)
    # Also save keras model.
    model.save(model.name)

    # Convert the model.
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    # Include necessary functions.
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
                                           tf.lite.OpsSet.SELECT_TF_OPS]
    tflite_model = converter.convert()

    # Save the model.
    with open('model.tflite', 'wb') as f:
        f.write(tflite_model)

    # Load the TFLite model from disk and allocate tensors.
    interpreter = tf.lite.Interpreter(model_path="model.tflite")
    interpreter.allocate_tensors()

    # Get input and output tensors.
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # Test model ouput on the same data to ensure correct function. 
    input_shape = input_details[0]['shape']
    input_data = np.ones((input_shape), dtype=np.float32) * 0.5
    interpreter.set_tensor(input_details[0]['index'], input_data)

    interpreter.invoke()

    # The function `get_tensor()` returns a copy of the tensor data.
    # Use `tensor()` in order to get a pointer to the tensor.
    output_data = interpreter.get_tensor(output_details[0]['index'])
    print(output_data.shape)

Thanks.