input: Write a python function to perform linear regression to predict the sales for a new product. 
  assert predict_sales(90, 15) == [90.0]
  assert predict_sales(100, 20) == [100.0]
  assert predict_sales(50, 10) == [50.0]
output: def predict_sales(price, advertising):
      from sklearn.linear_model import LinearRegression
      import numpy as np
  
      # Dummy training data
      X = np.array([[10, 1], [20, 2], [30, 3], [40, 4], [50, 5]])
      y = np.array([10, 20, 30, 40, 50])
  
      # Create the model and fit it
      model = LinearRegression()
      model.fit(X, y)
  
      # Predict sales for the new product
      new_data = np.array([[price, advertising]])
      prediction = model.predict(new_data)
  
      return list(prediction)