input: Write a python function to create, compile, train, and save a neural network model using TensorFlow. 
  assert type(create_and_train_model(X_train, y_train, X_test, y_test, 'weather_predictor_model.h5')) == tf.keras.Sequential
output: def create_and_train_model(X_train, y_train, X_test, y_test, model_filename):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(64, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
  
      model.save(model_filename)
  
      return model