input: Write a Python function to create and train a linear regression model using the given input data, and return the model's coefficients and predictions on a test dataset.
  assert create_and_train_model(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]), np.array([1, 2, 3, 4]), np.array([[3, 4], [5, 6]])) == (np.array([0.5, 0.5]), np.array([2., 3.]))
  assert create_and_train_model(np.array([[2, 3], [4, 5], [6, 7], [8, 9]]), np.array([2, 4, 6, 8]), np.array([[4, 5], [6, 7]])) == (np.array([1., 1.]), np.array([4., 6.]))
  assert create_and_train_model(np.array([[0, 1], [1, 2], [2, 3], [3, 4]]), np.array([0, 1, 2, 3]), np.array([[1, 2], [2, 3]])) == (np.array([0.5, 0.5]), np.array([1., 2.]))
output: def create_and_train_model(X_train, y_train, X_test):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      # Create the model
      model = LinearRegression()
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Get the coefficients
      coefficients = model.coef_
      
      # Make predictions
      predictions = model.predict(X_test)
      
      return coefficients, predictions