input: Write a python function to create and fit a linear regression model using given data, and then make a prediction using the model.
  ```python
  import numpy as np
  from sklearn.linear_model import LinearRegression
  
  def create_and_predict(x_train, y_train, x_test):
      # Create a linear regression model
      model = LinearRegression()
      # Fit the model
      model.fit(x_train, y_train)
      # Make prediction
      y_pred = model.predict([x_test])
      return y_pred[0]
  
  x_train = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
  y_train = np.dot(x_train, np.array([1, 2])) + 3
  
  assert np.isclose(create_and_predict(x_train, y_train, [3, 5]), 16.0)
  assert np.isclose(create_and_predict(x_train, y_train, [2, 1]), 8.0)
  assert np.isclose(create_and_predict(x_train, y_train, [4, 4]), 17.0)
output: def create_and_predict(x_train, y_train, x_test):
      # Create a linear regression model
      model = LinearRegression()
      # Fit the model
      model.fit(x_train, y_train)
      # Make prediction
      y_pred = model.predict([x_test])
      return y_pred[0]
  
  x_train = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
  y_train = np.dot(x_train, np.array([1, 2])) + 3
  
  assert np.isclose(create_and_predict(x_train, y_train, [3, 5]), 16.0)
  assert np.isclose(create_and_predict(x_train, y_train, [2, 1]), 8.0)
  assert np.isclose(create_and_predict(x_train, y_train, [4, 4]), 17.0)