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. The model should be built using TensorFlow and should be trained to minimize mean squared error.
  
  if __name__ == "__main__":
      model = create_model()
  
      input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
      predictions = model.predict(input_data)
  
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")
  
  assert create_model() is not None
  assert predictions[0][0] is not None
output: 
  import tensorflow as tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def create_model():
      model = Sequential([
          Dense(64, activation='relu', input_shape=(7,)),
          Dense(32, activation='relu'),
          Dense(1)
      ])
      model.compile(optimizer='adam', loss='mean_squared_error')
      return model
  