input: Write a python function to create and run a weighted linear regression using scikit-learn's LinearRegression.
  assert run_weighted_regression(X=[[1, 1], [1, 2], [2, 2], [2, 3]], y=[1, 2, 2, 3], weight=[1, 1, 1, 1]) == ([0.5, 0.5], 0.5)
  assert run_weighted_regression(X=[[1, 1], [1, 2], [2, 2], [2, 3]], y=[2, 3, 3, 4], weight=[2, 1, 1, 2]) == ([0.5, 1.0], 1.0)
output: def run_weighted_regression(X, y, weight):
      import numpy as np
      from sklearn.linear_model import LinearRegression
  
      # Convert weights to a diagonal matrix
      W = np.diag(weight)
      
      # Compute the weighted X and y
      X_weighted = np.dot(W, X)
      y_weighted = np.dot(W, y)
      
      # Create and fit the weighted linear regression model
      model = LinearRegression().fit(X_weighted, y_weighted)
      
      # Get the coefficients and intercept
      coefficients = model.coef_
      intercept = model.intercept_
      
      return list(coefficients), intercept