text: from data_iter import DisDataIter, GenDataIter
  from Discriminator import Discriminator
  from Generator import Generator
  from utils import recurrent_func, loss_func, get_sample, get_rewards
  
  from torch.autograd import Variable
  from torch.nn.utils import clip_grad_norm
  import glob
  import json
  import numpy as np
  import os
  
  import torch
  import torch.nn as nn
  import torch.optim as optim
  
  def get_params(filePath):
      with open(filePath, 'r') as f:
          params = json.load(f)
      f.close()
      return params
  
  def get_arguments():
      train_params = get_params("./params/train_params.json")
      leak_gan_params = get_params("./params/leak_gan_params.json")
      target_params = get_params("./params/target_params.json")
      dis_data_params = get_params("./params/dis_data_params.json")
      real_data_params = get_params("./params/real_data_params.json")
      return {
          "train_params": train_params,
          "leak_gan_params": leak_gan_params,
          "target_params": target_params,
          "dis_data_params": dis_data_params,
          "real_data_params" : real_data_params
      }
  """
  def prepare_model_dict(use_cuda=False):
      f = open()
  """