input: Write a function to create a neural network model that takes a given trajectory of stock prices as input and predicts the stock price for the next day. The model should be a sequential model with a Dense layer with 64 neurons and ReLU activation, followed by an output Dense layer with one neuron. Compile the model with mean squared error loss, Adam optimizer, and accuracy metric. Train the model with the given input data. If the model is empty or not properly compiled, return None.
  assert create_model==[[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]
output: 
  def create_model(stock_prices):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(64, input_dim=len(stock_prices), activation='relu'))
      model.add(Dense(1))
      
      if not model.built:
          return None
      
      model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
      
      return model
  