input: 
  def create_neural_network_model(input_trajectory: list) -> float:
      """ This function creates a neural network model using TensorFlow and Keras to take in a 
      given trajectory of stock prices and predict the stock price for the next day.
      >>> create_neural_network_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.0
      """
  
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      X = tf.constant(input_trajectory[:-1], dtype=tf.float32)
      y = tf.constant(input_trajectory[1:], dtype=tf.float32)
  
      # Create the model
      model = Sequential([
          Dense(10, input_shape=(1,), activation='relu'),
          Dense(1)
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=50, verbose=0)
  
      # Predict the next day's stock price
      next_day_price = model.predict(tf.constant([input_trajectory[-1]], dtype=tf.float32))
  
      return next_day_price[0][0].numpy()