input: Write a python function to perform linear regression on historical stock prices and predict future prices.
  assert len(predict_stock_prices('sample_stock_prices.csv')) > 0
  assert type(predict_stock_prices('sample_stock_prices.csv')) == list
output: import pandas as pd
  from sklearn.linear_model import LinearRegression
  import numpy as np
  
  def predict_stock_prices(file_path):
      # Load the historical stock prices
      data = pd.read_csv(file_path)
      
      # Assuming the CSV has a 'Date' and 'Price' column
      data['Date'] = pd.to_datetime(data['Date'])
      data['Date'] = data['Date'].map(pd.Timestamp.toordinal)
      
      X = data['Date'].values.reshape(-1, 1)
      y = data['Price'].values
      
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Predict future stock prices (next 10 days)
      future_dates = np.arange(X[-1] + 1, X[-1] + 11).reshape(-1, 1)
      predictions = model.predict(future_dates)
      
      return predictions.tolist()