input: Write a python function to predict the price of a taxi ride based on the distance and time using Linear Regression.
  assert abs(predict_taxi_price(10, 30) - 30) < 1e-2
  assert abs(predict_taxi_price(20, 40) - 40) < 1e-2
  assert abs(predict_taxi_price(30, 50) - 50) < 1e-2
output: def predict_taxi_price(distance, time):
      from sklearn.linear_model import LinearRegression
      import numpy as np
  
      # Training data (hypothetical)
      X = np.array([
          [10, 30],
          [20, 40],
          [30, 50]
      ])
      y = np.array([30, 40, 50])
  
      # Create and train the model
      model = LinearRegression()
      model.fit(X, y)
  
      # Predict the price based on input distance and time
      price = model.predict(np.array([[distance, time]]))[0]
  
      return price