input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day.
  
  import numpy as np
  import tensorflow as tf
  
  def create_model():
      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'])
  
      return model
  
  assert create_model() is not None
  input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
  model = create_model()
  predictions = model.predict(input_data)
  assert predictions[0][0] is not None
output: def create_model():
      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'])
  
      return model