text: import tempfile
  
  import pytest
  import torch
  import torch.nn as nn
  from mmcv import Config
  from torch.utils.data import Dataset
  
  from mmaction.apis import train_model
  from mmaction.datasets.registry import DATASETS
  
  
  @DATASETS.register_module()
  class ExampleDataset(Dataset):
  
      def __init__(self, test_mode=False):
          self.test_mode = test_mode
  
      def evaluate(self, results, logger=None):
          eval_results = dict()
          eval_results['acc'] = 1
          return eval_results
  
      def __getitem__(self, idx):
          results = dict(imgs=torch.tensor([1]))
          return results
  
      def __len__(self):
          return 1
  
  
  class ExampleModel(nn.Module):
  
      def __init__(self):
          super().__init__()
          self.test_cfg = None
          self.conv1 = nn.Conv2d(3, 8, kernel_size=1)
          self.norm1 = nn.BatchNorm1d(2)
  
      def forward(self, imgs, return_loss=False):
          self.norm1(torch.rand(3, 2).cuda())
          losses = dict()
          losses['test_loss'] = torch.tensor([0.5], requires_grad=True)
          return losses
  
      def train_step(self, data_batch, optimizer, **kwargs):
          imgs = data_batch['imgs']
          losses = self.forward(imgs, True)
          loss = torch.tensor([0.5], requires_grad=True)
          outputs = dict(loss=loss, log_vars=losses, num_samples=3)
          return outputs
  
      def val_step(self, data_batch, optimizer, **kwargs):
          imgs = data_batch['imgs']
          self.forward(imgs, False)
          outputs = dict(results=0.5)
          return outputs
  
  
  @pytest.mark.skipif(
      not torch.cuda.is_available(), reason='requires CUDA support')
  def test_train_model():
      model = ExampleModel()
      dataset = ExampleDataset()
      datasets = [ExampleDataset(), ExampleDataset()]
      cfg = dict(
          seed=0,
          gpus=1,
          gpu_ids=[0],
          resume_from=None,
          load_from=None,
          workflow=[('train', 1)],
          total_epochs=5,
          evaluation=dict(interval=1, key_indicator='acc'),
          data=dict(
              videos_per_gpu=1,
              workers_per_gpu=0,
              val=dict(type='ExampleDataset')),
          optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
          optimizer_config=dict(grad_clip=dict(max_norm=40, norm_type=2)),
          lr_config=dict(policy='step', step=[40, 80]),
          omnisource=False,
          precise_bn=False,
          checkpoint_config=dict(interval=1),
          log_level='INFO',
          log_config=dict(interval=20, hooks=[dict(type='TextLoggerHook')]))
  
      with tempfile.TemporaryDirectory() as tmpdir:
          # normal train
          cfg['work_dir'] = tmpdir
          config = Config(cfg)
          train_model(model, dataset, config)
  
      with tempfile.TemporaryDirectory() as tmpdir:
          # train with validation
          cfg['work_dir'] = tmpdir
          config = Config(cfg)
          train_model(model, dataset, config, validate=True)
  
      with tempfile.TemporaryDirectory() as tmpdir:
          # train with Fp16OptimizerHook
          cfg['work_dir'] = tmpdir
          cfg['fp16'] = dict(loss_scale=512.)
          config = Config(cfg)
          model.fp16_enabled = None
          train_model(model, dataset, config)
  
      with tempfile.TemporaryDirectory() as tmpdir:
          cfg['work_dir'] = tmpdir
          cfg['omnisource'] = True
          config = Config(cfg)
          train_model(model, datasets, config)
  
      with tempfile.TemporaryDirectory() as tmpdir:
          # train with precise_bn on
          cfg['work_dir'] = tmpdir
          cfg['precise_bn'] = dict(num_iters=1, interval=1)
          config = Config(cfg)
          train_model(model, datasets, config)
  