text: import argparse
  import torch
  import torch.utils.data
  import torch.nn as nn
  import torch.optim as optim
  from torch.autograd import Variable
  from torchvision import datasets, transforms
  from fastparquet import ParquetFile
  import os
  import numpy as np
  
  
  parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
  parser.add_argument('--batch-size', type=int, default=128, metavar='N',
                      help='input batch size for training (default: 64)')
  parser.add_argument('--epochs', type=int, default=10, metavar='N',
                      help='number of epochs to train (default: 2)')
  parser.add_argument('--no-cuda', action='store_true', default=False,
                      help='enables CUDA training')
  parser.add_argument('--seed', type=int, default=1, metavar='S',
                      help='random seed (default: 1)')
  parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                      help='how many batches to wait before logging training status')
  args = parser.parse_args()
  args.cuda = not args.no_cuda and torch.cuda.is_available()
  
  
  torch.manual_seed(args.seed)
  if args.cuda:
      torch.cuda.manual_seed(args.seed)
  
  # Load data
  project_dir = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)
  data_dir = os.path.join(project_dir, 'data', 'interim', 'data.parq')
  pf = ParquetFile(data_dir)
  data = pf.to_pandas()
  
  kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
  train_loader = torch.utils.data.DataLoader(data.values, batch_size=args.batch_size, shuffle=True, **kwargs)
  test_loader = torch.utils.data.DataLoader( data.values, batch_size=args.batch_size, shuffle=True, **kwargs)
  
  
  # Params
  noise_variance_var = 0.1
  
  
  class ImplicitDenoising(nn.Module):
      def __init__(self, num_z=3):
          super(ImplicitDenoising, self).__init__()
          self.relu = nn.ReLU()
          self.sigmoid = nn.Sigmoid()
          self.num_z = num_z
  
          self.g1 = nn.Linear(self.num_z, 20)
          self.g2 = nn.Linear(20, 10)
          self.g3 = nn.Linear(10, 2)
  
          self.d1 = nn.Linear(2, 20)
          self.d2 = nn.Linear(20, 10)
          self.d3 = nn.Linear(10, 20)
          self.d4 = nn.Linear(20, 2)
  
      def generator(self, z):
          z = self.relu(self.g1(z))
          z = self.relu(self.g2(z))
          return self.g3(z)
  
      def denoiser(self, z):
          z = self.relu(self.d1(z))
          z = self.relu(self.d2(z))
          z = self.relu(self.d3(z))
          return self.d4(z)
  
      def forward(self, x):
  
          z_rnd = torch.randn(args.batch_size, self.num_z)
          epsilon_rnd = torch.sqrt(noise_variance_var) * torch.randn(args.batchsize, 2)
  
          samples_from_generator = self.generator(z_rnd)
          noisy_samples_from_generator = samples_from_generator + epsilon_rnd
  
          denoised_samples_from_generator = self.denoiser(samples_from_generator)
          denoised_noisy_samples_from_generator = self.denoiser(noisy_samples_from_generator)
          return denoised_samples_from_generator, denoised_noisy_samples_from_generator
  
  
  model = ImplicitDenoising()
  if args.cuda:
      model.cuda()
  
  loss_denoiser = nn.MSELoss()
  sigmoid = nn.Sigmoid()
  
  optimizer = optim.Adam(model.parameters(), lr=1e-3)
  
  
  def train(epoch):
      model.train()
      train_loss = 0
      for batch_idx, (data, _) in enumerate(train_loader):
          data = Variable(data)
          if args.cuda:
              data = data.cuda()
          optimizer.zero_grad()
          recon_batch, mu, logvar = model(data)
          loss = loss_denoiser(recon_batch, data)
          loss.backward()
          train_loss += loss.data[0]
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                  epoch, batch_idx * len(data), len(train_loader.dataset),
                  100. * batch_idx / len(train_loader),
                  loss.data[0] / len(data)))
  
      print('====> Epoch: {} Average loss: {:.4f}'.format(
            epoch, train_loss / len(train_loader.dataset)))
  
  
  def test(epoch):
      model.eval()
      test_loss = 0
      for data, _ in test_loader:
          if args.cuda:
              data = data.cuda()
          data = Variable(data, volatile=True)
          recon_batch, mu, logvar = model(data)
          test_loss += loss_denoiser(recon_batch, data).data[0]
  
      test_loss /= len(test_loader.dataset)
      print('====> Test set loss: {:.4f}'.format(test_loss))
  
  
  for epoch in range(1, args.epochs + 1):
      train(epoch)
      test(epoch)