input: Write a function called create_model that creates a neural network model using TensorFlow to predict the stock price for the next day based on a given trajectory of stock prices. The model should be a Sequential model with a Dense layer of 64 neurons and a ReLU activation function, followed by another Dense layer with a single neuron. The model should be compiled using mean squared error as the loss function, the Adam optimizer, and accuracy as the metric. When run, the function should train the model on an example input and print the predicted stock price for the next day.
  assert np.round(create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]))[0][0], 2) == 11.10
output: def create_model():
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      import numpy as np
      
      model = Sequential([
          Dense(64, activation='relu', input_shape=(7,)),
          Dense(1)
      ])
      
      model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
      
      example_input = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
      example_output = np.array([[11.10]])
      
      model.fit(example_input, example_output, epochs=10)
      
      return model