input: Write a function to create a Python neural network model that takes in a trajectory of stock prices and predicts the stock price for the next day. The model should be based on TensorFlow and Keras, specifically using a Sequential model with two Dense layers. The input shape should be set to (7, ) to accommodate a 7-day stock price trajectory.
  
  
  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
  
  # Test case 1
  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 isinstance(predictions, np.ndarray) and predictions.shape == (1, 1)
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")
  
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