input: Write a function to create a Deep Learning model in Python using Keras to predict stock prices. The function should define the model architecture, compile it, and train it on the provided dataset. Return the trained model after training.
  assert create_stock_price_model().history.params['epochs'] == 50
  assert create_stock_price_model().history.params['batch_size'] == 32
  assert len(create_stock_price_model().history.history['loss']) == 50
  assert len(create_stock_price_model().history.history['val_loss']) == 50
output: def create_stock_price_model():
      from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np
      
      # Example dataset
      X_train = np.random.rand(1000, 10)
      y_train = np.random.rand(1000, 1)
      X_val = np.random.rand(200, 10)
      y_val = np.random.rand(200, 1)
      
      model = Sequential()
      model.add(Dense(64, input_dim=10, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
      
      model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])
      
      history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, batch_size=32)
      
      return model