input: import numpy as np
  import tensorflow as tf
  
  def predict_stock_price_next_day(inputs: List[float]) -> float:
      """ Take a list of stock prices for consecutive days and predict the stock
      price for the next day. The model used is a simple neural network with one hidden layer.
      >>> predict_stock_price_next_day([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.21
      """
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      input_data = np.array([inputs])
      predictions = model.predict(input_data)
  
      return predictions[0][0]
output:     model.fit(np.array([inputs]), np.array([inputs[-1]]), epochs=100, verbose=0)