input: Write a function to create a neural network model using TensorFlow that takes a trajectory of stock prices as input and predicts the stock price for the next day. The function should return the created model. Additionally, provide test cases that demonstrate how the model can be used to make predictions.
  
  assert create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]))[0][0]==11.1
  assert create_model().predict(np.array([[20, 20.6, 20.7, 20.8, 21.0, 21.2, 21.5]]))[0][0]==21.7
  assert create_model().predict(np.array([[5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6]]))[0][0]==5.7
output: 
  import tensorflow as tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def create_model():
      model = Sequential([
          Dense(10, input_shape=(7,), activation='relu'),
          Dense(1, activation='linear')
      ])
      model.compile(optimizer='adam', loss='mean_squared_error')
      return model
  