input: Write a function to create a neural network model that takes in a sequence of stock prices and predicts the stock price for the next day. Ensure the function returns the trained model. If the input sequence length is less than 7, the function should raise a ValueError.
  import numpy as np
  import tensorflow as tf
  
  def create_model(input_data):
      if len(input_data) < 7:
          raise ValueError("Input sequence length should be at least 7")
      
      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'])
  
      input_data = np.array([input_data])
      model.fit(input_data, np.array([input_data[-1]]), epochs=10, verbose=0)
  
      return model
      
  assert isinstance(create_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]), tf.keras.Sequential)
  assert create_model([10])
  
output: def create_model(input_data):
      if len(input_data) < 7:
          raise ValueError("Input sequence length should be at least 7")
      
      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'])
  
      input_data = np.array([input_data])
      model.fit(input_data, np.array([input_data[-1]]), epochs=10, verbose=0)
  
      return model