input: Write a function named `create_model` that creates a neural network model using TensorFlow to predict the next day's stock price based on a given trajectory of stock prices. This model should take in a trajectory of 7 consecutive stock prices and output a single predicted stock price for the next day. The function should return the compiled model, and in the main section, it should use the provided input data to predict the next day's stock price and print the result.
  
  assert isinstance(create_model(), tf.keras.models.Sequential)
  assert create_model().compile_kwargs == {'loss': 'mean_squared_error', 'optimizer': 'adam', 'metrics': ['accuracy']}
  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)
      assert len(predictions[0]) == 1
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")
output: def create_model():
      model = tf.keras.models.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7,)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
      model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
      return model