input: Write a function to create a Python neural network model that takes a given trajectory of stock prices and predicts the stock price for the next day. Implement the model using TensorFlow and provide an example of how to use the model to make a prediction.
  assert create_model()[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]==[11.1]
  assert create_model()[9.8, 10.2, 10.3, 10.5, 10.6, 10.8, 11.0]==[11.1]
  assert create_model()[10.1, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8]==[10.9]
  assert create_model()[11.0, 11.2, 11.3, 11.4, 11.5, 11.7, 11.8]==[11.9]
  assert create_model()[[10.0, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0]]==[11.1]
  assert create_model()[[10.3, 10.7, 10.8, 10.9, 11.0, 11.1, 11.2]]==[11.3]
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)
      ])
      model.compile(optimizer='adam', loss='mse')
      return model
  