text: import os
  from collections import OrderedDict
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch import optim
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from torchvision import transforms
  from torchvision.datasets import MNIST
  
  try:
      from test_tube import HyperOptArgumentParser
  except ImportError:
      # TODO: this should be discussed and moved out of this package
      raise ImportError('Missing test-tube package.')
  
  from pytorch_lightning.core.decorators import data_loader
  from pytorch_lightning.core.lightning import LightningModule
  
  
  class TestingMNIST(MNIST):
  
      def __init__(self, root, train=True, transform=None, target_transform=None,
                   download=False, num_samples=8000):
          super(TestingMNIST, self).__init__(
              root,
              train=train,
              transform=transform,
              target_transform=target_transform,
              download=download
          )
          # take just a subset of MNIST dataset
          self.data = self.data[:num_samples]
          self.targets = self.targets[:num_samples]
  
  
  class TestModelBase(LightningModule):
      """
      Base LightningModule for testing. Implements only the required
      interface
      """
  
      def __init__(self, hparams, force_remove_distributed_sampler=False):
          """
          Pass in parsed HyperOptArgumentParser to the model
          :param hparams:
          """
          # init superclass
          super(TestModelBase, self).__init__()
          self.hparams = hparams
  
          self.batch_size = hparams.batch_size
  
          # if you specify an example input, the summary will show input/output for each layer
          self.example_input_array = torch.rand(5, 28 * 28)
  
          # remove to test warning for dist sampler
          self.force_remove_distributed_sampler = force_remove_distributed_sampler
  
          # build model
          self.__build_model()
  
      # ---------------------
      # MODEL SETUP
      # ---------------------
      def __build_model(self):
          """
          Layout model
          :return:
          """
          self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
                                out_features=self.hparams.hidden_dim)
          self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
          self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
  
          self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
                                out_features=self.hparams.out_features)
  
      # ---------------------
      # TRAINING
      # ---------------------
      def forward(self, x):
          """
          No special modification required for lightning, define as you normally would
          :param x:
          :return:
          """
  
          x = self.c_d1(x)
          x = torch.tanh(x)
          x = self.c_d1_bn(x)
          x = self.c_d1_drop(x)
  
          x = self.c_d2(x)
          logits = F.log_softmax(x, dim=1)
  
          return logits
  
      def loss(self, labels, logits):
          nll = F.nll_loss(logits, labels)
          return nll
  
      def training_step(self, batch, batch_idx):
          """
          Lightning calls this inside the training loop
          :param batch:
          :return:
          """
          # forward pass
          x, y = batch
          x = x.view(x.size(0), -1)
  
          y_hat = self.forward(x)
  
          # calculate loss
          loss_val = self.loss(y, y_hat)
  
          # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
          if self.trainer.use_dp:
              loss_val = loss_val.unsqueeze(0)
  
          # alternate possible outputs to test
          if self.trainer.batch_idx % 1 == 0:
              output = OrderedDict({
                  'loss': loss_val,
                  'progress_bar': {'some_val': loss_val * loss_val},
                  'log': {'train_some_val': loss_val * loss_val},
              })
  
              return output
          if self.trainer.batch_idx % 2 == 0:
              return loss_val
  
      # ---------------------
      # TRAINING SETUP
      # ---------------------
      def configure_optimizers(self):
          """
          return whatever optimizers we want here.
          :return: list of optimizers
          """
          # try no scheduler for this model (testing purposes)
          if self.hparams.optimizer_name == 'lbfgs':
              optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate)
          else:
              optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
  
          # test returning only 1 list instead of 2
          return optimizer
  
      def prepare_data(self):
          transform = transforms.Compose([transforms.ToTensor(),
                                          transforms.Normalize((0.5,), (1.0,))])
          dataset = TestingMNIST(root=self.hparams.data_root, train=True,
                                 transform=transform, download=True, num_samples=2000)
          dataset = TestingMNIST(root=self.hparams.data_root, train=False,
                                 transform=transform, download=True, num_samples=2000)
  
      def _dataloader(self, train):
          # init data generators
          transform = transforms.Compose([transforms.ToTensor(),
                                          transforms.Normalize((0.5,), (1.0,))])
          dataset = TestingMNIST(root=self.hparams.data_root, train=train,
                                 transform=transform, download=False, num_samples=2000)
  
          # when using multi-node we need to add the datasampler
          batch_size = self.hparams.batch_size
  
          loader = DataLoader(
              dataset=dataset,
              batch_size=batch_size,
          )
  
          return loader
  
      @staticmethod
      def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
          """
          Parameters you define here will be available to your model through self.hparams
          :param parent_parser:
          :param root_dir:
          :return:
          """
          parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
  
          # param overwrites
          # parser.set_defaults(gradient_clip_val=5.0)
  
          # network params
          parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
          parser.add_argument('--in_features', default=28 * 28, type=int)
          parser.add_argument('--out_features', default=10, type=int)
          # use 500 for CPU, 50000 for GPU to see speed difference
          parser.add_argument('--hidden_dim', default=50000, type=int)
  
          # data
          parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
  
          # training params (opt)
          parser.opt_list('--learning_rate', default=0.001 * 8, type=float,
                          options=[0.0001, 0.0005, 0.001, 0.005],
                          tunable=False)
          parser.opt_list('--optimizer_name', default='adam', type=str,
                          options=['adam'], tunable=False)
  
          # if using 2 nodes with 4 gpus each the batch size here
          #  (256) will be 256 / (2*8) = 16 per gpu
          parser.opt_list('--batch_size', default=256 * 8, type=int,
                          options=[32, 64, 128, 256], tunable=False,
                          help='batch size will be divided over all gpus being used across all nodes')
          return parser
  
  
  class LightningTestModelBase(TestModelBase):
      """ with pre-defined train dataloader """
      def train_dataloader(self):
          return self._dataloader(train=True)
  
  
  class LightningTestModelBaseWithoutDataloader(TestModelBase):
      """ without pre-defined train dataloader """
      pass
  