input: Write a python function to load user data from a CSV file and split it into training and testing sets.
  assert len(split_data('user_data.csv', 0.2)['x_train']) == 0.8 * len(pd.read_csv('user_data.csv'))
  assert len(split_data('user_data.csv', 0.2)['x_test']) == 0.2 * len(pd.read_csv('user_data.csv'))
  assert 'age' in split_data('user_data.csv', 0.2)['x_train'].columns
  assert 'click' in split_data('user_data.csv', 0.2)['y_train'].name
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  
  def split_data(file_path, test_size):
      data = pd.read_csv(file_path)
      X = data.drop('click', axis=1)
      y = data['click']
      
      x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
      
      return {
          'x_train': x_train,
          'x_test': x_test,
          'y_train': y_train,
          'y_test': y_test
      }