code: 
  import os
  import click
  import re
  import json
  import tempfile
  import torch
  import dnnlib
  
  from training import training_loop
  from metrics import metric_main
  from torch_utils import training_stats
  from torch_utils import custom_ops
  
  #----------------------------------------------------------------------------
  
  class UserError(Exception):
      pass
  
  #----------------------------------------------------------------------------
  
  def setup_training_loop_kwargs(
      f_dim      = None,
      d_use_norm = None, # normalize the feature extracted by discriminator or not
      d_use_fts  = None, # discriminator extract semantic feature or not
      mixing_prob= None, # mixing probability of ground-truth and language-free generated pairs, mixing_prob=0 means only use ground-truth, mixing_prob=1. means using only pseudo pairs(language-free)
      lam        = None, # hyper-parameter for contrastive loss
      temp       = None, # hyper-parameter for contrastive loss
      change     = None, # hyper-parameter for architecture
      map_num    = None, # hyper-parameter for architecture
      gather     = None, # hyper-parameter for contrastive loss
      itd        = None, # hyper-parameter for contrastive loss
      itc        = None, # hyper-parameter for contrastive loss
      iid        = None, # hyper-parameter for contrastive loss
      iic        = None, # hyper-parameter for contrastive loss
      metric_only_test = None, # hyper-parameter for computing metrics
      fmap       = None, # hyper-parameter for architecture, related to channel number
      ratio      = None,
      # General options (not included in desc).
      gpus       = None, # Number of GPUs: <int>, default = 1 gpu
      snap       = None, # Snapshot interval: <int>, default = 50 ticks
      metrics    = None, # List of metric names: [], ['fid50k_full'] (default), ...
      seed       = None, # Random seed: <int>, default = 0
      # Dataset.
      data       = None, # Training dataset (required): <path>
      test_data  = None, # Testing dataset for metrics, if not use training dataset
      cond       = None, # Train conditional model based on dataset labels: <bool>, default = False
      subset     = None, # Train with only N images: <int>, default = all
      mirror     = None, # Augment dataset with x-flips: <bool>, default = False
  
      # Base config.
      cfg        = None, # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar'
      gamma      = None, # Override R1 gamma: <float>
      kimg       = None, # Override training duration: <int>
      batch      = None, # Override batch size: <int>
  
      # Discriminator augmentation.
      aug        = None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed'
      p          = None, # Specify p for 'fixed' (required): <float>
      target     = None, # Override ADA target for 'ada': <float>, default = depends on aug
      augpipe    = None, # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
  
      # Transfer learning.
      resume     = None, # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', <file>, <url>
      freezed    = None, # Freeze-D: <int>, default = 0 discriminator layers
  
      # Performance options (not included in desc).
      fp32       = None, # Disable mixed-precision training: <bool>, default = False
      nhwc       = None, # Use NHWC memory format with FP16: <bool>, default = False
      allow_tf32 = None, # Allow PyTorch to use TF32 for matmul and convolutions: <bool>, default = False
      nobench    = None, # Disable cuDNN benchmarking: <bool>, default = False
      workers    = None, # Override number of DataLoader workers: <int>, default = 3
  ):
      args = dnnlib.EasyDict()
  
      # ------------------------------------------
      # General options: gpus, snap, metrics, seed
      # ------------------------------------------
      if f_dim is None:
          f_dim = 512
      assert isinstance(f_dim, int)
      args.f_dim = f_dim
  
      if ratio is None:
          ratio = 1.0
      args.ratio = ratio
      
      if mixing_prob is None:
          mixing_prob = 0.
      args.mixing_prob = mixing_prob
      
      if fmap is None:
          fmap = 1.
      
      if metric_only_test is None:
          metric_only_test = False
      args.metric_only_test = metric_only_test
      
      if map_num is None:
          map_num = 8
          
      if lam is None:
          lam = 0.
      args.lam = lam
      
      if temp is None:
          temp = 0.5
      args.temp = temp
      
      if itd is None:
          itd = 10.
      args.itd = itd
      if itc is None:
          itc = 10.
      args.itc = itc
      
      if iid is None:
          iid = 0.
      args.iid = iid
      if iic is None:
          iic = 0.
      args.iic = iic
      
      
      
      if change is None:
          change = 256
      
      if d_use_norm is None:
          d_use_norm = False
      assert isinstance(d_use_norm, bool)
      args.d_use_norm = d_use_norm
      
      if d_use_fts is None:
          d_use_fts = True
      args.d_use_fts = d_use_fts
      
      if gather is None:
          gather = False
      args.gather = gather
      
      if gpus is None:
          gpus = 1
      assert isinstance(gpus, int)
      if not (gpus >= 1 and gpus & (gpus - 1) == 0):
          raise UserError('--gpus must be a power of two')
      args.num_gpus = gpus
  
      if snap is None:
          snap = 50
      assert isinstance(snap, int)
      if snap < 1:
          raise UserError('--snap must be at least 1')
      args.image_snapshot_ticks = snap
      args.network_snapshot_ticks = snap
  
      if metrics is None:
          metrics = ['fid50k_full']
      assert isinstance(metrics, list)
      if not all(metric_main.is_valid_metric(metric) for metric in metrics):
          raise UserError('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
      args.metrics = metrics
  
      if seed is None:
          seed = 0
      assert isinstance(seed, int)
      args.random_seed = seed
  
      # -----------------------------------
      # Dataset: data, cond, subset, mirror
      # -----------------------------------
  
      assert data is not None
      assert isinstance(data, str)
      print('using data: ', data, 'testing data: ', test_data)
      if test_data is None:
          test_data = data
      args.training_set_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False, use_clip=True, ratio=args.ratio)
      args.testing_set_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=test_data, use_labels=True, max_size=None, xflip=False, use_clip=True, ratio=1.0)
      args.data_loader_kwargs = dnnlib.EasyDict(pin_memory=False, num_workers=1, prefetch_factor=2)
      try:
          training_set = dnnlib.util.construct_class_by_name(**args.training_set_kwargs) # subclass of training.dataset.Dataset
          args.training_set_kwargs.resolution = training_set.resolution # be explicit about resolution
          args.training_set_kwargs.use_labels = training_set.has_labels # be explicit about labels
          args.training_set_kwargs.max_size = len(training_set) # be explicit about dataset size
          desc = training_set.name
          args.testing_set_kwargs.resolution = training_set.resolution # be explicit about resolution
          args.testing_set_kwargs.use_labels = training_set.has_labels # be explicit about labels
          del training_set # conserve memory
  
      except IOError as err:
          raise UserError(f'--data: {err}')
  
      if cond is None:
          cond = False
      assert isinstance(cond, bool)
      if cond:
          if not args.training_set_kwargs.use_labels:
              raise UserError('--cond=True requires labels specified in dataset.json')
          desc += '-cond'
      else:
          args.training_set_kwargs.use_labels = False
          args.testing_set_kwargs.use_labels = False
  
      if subset is not None:
          assert isinstance(subset, int)
          if not 1 <= subset <= args.training_set_kwargs.max_size:
              raise UserError(f'--subset must be between 1 and {args.training_set_kwargs.max_size}')
          desc += f'-subset{subset}'
          if subset < args.training_set_kwargs.max_size:
              args.training_set_kwargs.max_size = subset
              args.training_set_kwargs.random_seed = args.random_seed
  
      if mirror is None:
          mirror = False
      assert isinstance(mirror, bool)
      if mirror:
          desc += '-mirror'
          args.training_set_kwargs.xflip = True
          args.testing_set_kwargs.xflip = True
  
      # ------------------------------------
      # Base config: cfg, gamma, kimg, batch
      # ------------------------------------
  
      if cfg is None:
          cfg = 'auto'
      assert isinstance(cfg, str)
      desc += f'-{cfg}-lam{lam:g}-temp{temp:g}-map_num{map_num:g}'
  
      cfg_specs = {
          'auto':      dict(ref_gpus=-1, kimg=25000,  mb=-1, mbstd=-1, fmaps=-1,  lrate=-1,     gamma=1.,   ema=-1,  ramp=0.05, map=map_num), # Populated dynamically based on resolution and GPU count.
      }
  
      assert cfg in cfg_specs
      spec = dnnlib.EasyDict(cfg_specs[cfg])
      if cfg == 'auto':
          desc += f'-gpus{gpus:d}'
          spec.ref_gpus = gpus
          res = args.training_set_kwargs.resolution
          spec.mb = 16*gpus#max(min(gpus * min(4096 // res, 32), 64), gpus) # keep gpu memory consumption at bay
          spec.mbstd = min(spec.mb // gpus, 4) # other hyperparams behave more predictably if mbstd group size remains fixed
          spec.fmaps = 1 if res >= 512 else fmap
          spec.lrate = 0.002 if res >= 1024 else 0.0025
          spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula
          spec.ema = spec.mb * 10 / 32
          
  #     args.M_kwargs = dnnlib.EasyDict(class_name='training.networks.ManiNetwork', z_dim=args.f_dim,  layer_features=args.f_dim, w_dim=512, num_layers=8)
      args.G_kwargs = dnnlib.EasyDict(class_name='training.networks.Generator', z_dim=512, w_dim=512, m_layer_features=args.f_dim, m_num_layers=8, mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict())
      args.D_kwargs = dnnlib.EasyDict(class_name='training.networks.Discriminator', use_norm=args.d_use_norm, use_fts=args.d_use_fts, block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
      args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int(spec.fmaps * 32768)
      args.G_kwargs.synthesis_kwargs.channel_max = args.D_kwargs.channel_max = 512
      args.G_kwargs.mapping_kwargs.num_layers = spec.map
      args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 4 # enable mixed-precision training
      args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = 256 # clamp activations to avoid float16 overflow
      args.G_kwargs.synthesis_kwargs.change = change
      args.G_kwargs.synthesis_kwargs.f_dim = args.f_dim
      args.D_kwargs.epilogue_kwargs.mbstd_group_size = spec.mbstd
      args.D_kwargs.epilogue_kwargs.f_dim = args.f_dim
      
      args.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8)
      args.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8)
      args.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss', r1_gamma=spec.gamma)
  
      args.total_kimg = spec.kimg
      args.batch_size = spec.mb
      args.batch_gpu = spec.mb // spec.ref_gpus
      args.ema_kimg = spec.ema
      args.ema_rampup = spec.ramp
  
      if cfg == 'cifar':
          args.loss_kwargs.pl_weight = 0 # disable path length regularization
          args.loss_kwargs.style_mixing_prob = 0 # disable style mixing
          args.D_kwargs.architecture = 'orig' # disable residual skip connections
  
      if gamma is not None:
          assert isinstance(gamma, float)
          if not gamma >= 0:
              raise UserError('--gamma must be non-negative')
          desc += f'-gamma{gamma:g}'
          args.loss_kwargs.r1_gamma = gamma
  
      if kimg is not None:
          assert isinstance(kimg, int)
          if not kimg >= 1:
              raise UserError('--kimg must be at least 1')
          desc += f'-kimg{kimg:d}'
          args.total_kimg = kimg
  
      if batch is not None:
          assert isinstance(batch, int)
          if not (batch >= 1 and batch % gpus == 0):
              raise UserError('--batch must be at least 1 and divisible by --gpus')
          desc += f'-batch{batch}'
          args.batch_size = batch
          args.batch_gpu = batch // gpus
  
      # ---------------------------------------------------
      # Discriminator augmentation: aug, p, target, augpipe
      # ---------------------------------------------------
  
      if aug is None:
          aug = 'noaug' # no augmentation is used in our experiments
      else:
          assert isinstance(aug, str)
          desc += f'-{aug}'
  
      if aug == 'ada':
          args.ada_target = 0.6
  
      elif aug == 'noaug':
          pass
  
      elif aug == 'fixed':
          if p is None:
              raise UserError(f'--aug={aug} requires specifying --p')
  
      else:
          raise UserError(f'--aug={aug} not supported')
  
      if p is not None:
          assert isinstance(p, float)
          if aug != 'fixed':
              raise UserError('--p can only be specified with --aug=fixed')
          if not 0 <= p <= 1:
              raise UserError('--p must be between 0 and 1')
          desc += f'-p{p:g}'
          args.augment_p = p
  
      if target is not None:
          assert isinstance(target, float)
          if aug != 'ada':
              raise UserError('--target can only be specified with --aug=ada')
          if not 0 <= target <= 1:
              raise UserError('--target must be between 0 and 1')
          desc += f'-target{target:g}'
          args.ada_target = target
  
      assert augpipe is None or isinstance(augpipe, str)
      if augpipe is None:
          augpipe = 'bgc'
      else:
          if aug == 'noaug':
              raise UserError('--augpipe cannot be specified with --aug=noaug')
          desc += f'-{augpipe}'
  
      augpipe_specs = {
          'blit':   dict(xflip=1, rotate90=1, xint=1),
          'geom':   dict(scale=1, rotate=1, aniso=1, xfrac=1),
          'color':  dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
          'filter': dict(imgfilter=1),
          'noise':  dict(noise=1),
          'cutout': dict(cutout=1),
          'bg':     dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1),
          'bgc':    dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
          'bgcf':   dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1),
          'bgfn':  dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, imgfilter=1, noise=1),
          'bgcfn':  dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1),
          'bgcfnc': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1, cutout=1),
      }
  
      assert augpipe in augpipe_specs
      if aug != 'noaug':
          args.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', **augpipe_specs[augpipe])
  
      # ----------------------------------
      # Transfer learning: resume, freezed
      # ----------------------------------
  
      resume_specs = {
          'ffhq256':     'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
          'ffhq512':     'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
          'ffhq1024':    'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
          'celebahq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
          'lsundog256':  'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
      }
  
      assert resume is None or isinstance(resume, str)
      if resume is None:
          resume = 'noresume'
      elif resume == 'noresume':
          desc += '-noresume'
      elif resume in resume_specs:
          desc += f'-resume{resume}'
          args.resume_pkl = resume_specs[resume] # predefined url
      else:
          desc += '-resumecustom'
          args.resume_pkl = resume # custom path or url
  
      if resume != 'noresume':
          args.ada_kimg = 100 # make ADA react faster at the beginning
          args.ema_rampup = None # disable EMA rampup
  
      if freezed is not None:
          assert isinstance(freezed, int)
          if not freezed >= 0:
              raise UserError('--freezed must be non-negative')
          desc += f'-freezed{freezed:d}'
          args.D_kwargs.block_kwargs.freeze_layers = freezed
  
      # -------------------------------------------------
      # Performance options: fp32, nhwc, nobench, workers
      # -------------------------------------------------
  
      if fp32 is None:
          fp32 = False
      assert isinstance(fp32, bool)
      if fp32:
          args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 0
          args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = None
  
      if nhwc is None:
          nhwc = False
      assert isinstance(nhwc, bool)
      if nhwc:
          args.G_kwargs.synthesis_kwargs.fp16_channels_last = args.D_kwargs.block_kwargs.fp16_channels_last = True
  
      if nobench is None:
          nobench = False
      assert isinstance(nobench, bool)
      if nobench:
          args.cudnn_benchmark = False
  
      if allow_tf32 is None:
          allow_tf32 = False
      assert isinstance(allow_tf32, bool)
      if allow_tf32:
          args.allow_tf32 = True
  
      if workers is not None:
          assert isinstance(workers, int)
          if not workers >= 1:
              raise UserError('--workers must be at least 1')
          args.data_loader_kwargs.num_workers = workers
  
      return desc, args
  
  #----------------------------------------------------------------------------
  
  def subprocess_fn(rank, args, temp_dir):
      dnnlib.util.Logger(file_name=os.path.join(args.run_dir, 'log.txt'), file_mode='a', should_flush=True)
  
      # Init torch.distributed.
      if args.num_gpus > 1:
          init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
          if os.name == 'nt':
              init_method = 'file:///' + init_file.replace('\\', '/')
              torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus)
          else:
              init_method = f'file://{init_file}'
              torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus)
  
      # Init torch_utils.
      sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None
      training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
      if rank != 0:
          custom_ops.verbosity = 'none'
  
      # Execute training loop.
      training_loop.training_loop(rank=rank, **args)
  
  #----------------------------------------------------------------------------
  
  class CommaSeparatedList(click.ParamType):
      name = 'list'
  
      def convert(self, value, param, ctx):
          _ = param, ctx
          if value is None or value.lower() == 'none' or value == '':
              return []
          return value.split(',')
  
  #----------------------------------------------------------------------------
  
  @click.command()
  @click.pass_context
  
  @click.option('--f_dim', help='dimension of features', type=int, metavar='INT')
  @click.option('--change', help='change structure', type=int, metavar='INT')
  @click.option('--map_num', help='layer number of mapping network', type=int, metavar='INT')
  @click.option('--d_use_norm', help='Input features into every layer of discriminator', type=bool, metavar='BOOL')
  @click.option('--d_use_fts', help='Use text feature in discriminator or not', type=bool, metavar='BOOL')
  @click.option('--gather', help='gather all negative samples across gpus or not', type=bool, metavar='BOOL')
  @click.option('--mixing_prob', help='if mixing_prob==1 -> no text data used', type=float)
  @click.option('--lam', help='hyper-parameter for contrastive loss (softmax along different dimensions)', type=float)
  @click.option('--temp', help='temperature for contrastive loss', type=float)
  @click.option('--itd', help='', type=float)
  @click.option('--itc', help='', type=float)
  @click.option('--iid', help='', type=float)
  @click.option('--iic', help='', type=float)
  @click.option('--metric_only_test', help='compute metrics using test dataset vs test dataset?', type=bool, metavar='BOOL')
  @click.option('--fmap', help='', type=float)
  @click.option('--ratio', help='ratio of data with ground-truth text used', type=float)
  
  
  # General options.
  @click.option('--outdir', help='Where to save the results', required=True, metavar='DIR')
  @click.option('--gpus', help='Number of GPUs to use [default: 1]', type=int, metavar='INT')
  @click.option('--snap', help='Snapshot interval [default: 50 ticks]', type=int, metavar='INT')
  @click.option('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList())
  @click.option('--seed', help='Random seed [default: 0]', type=int, metavar='INT')
  @click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True)
  
  # Dataset.
  @click.option('--data', help='Training data (directory or zip)', metavar='PATH', required=True)
  @click.option('--test_data', help='Testing data (directory or zip)', metavar='PATH', required=True)
  
  @click.option('--cond', help='Train conditional model based on dataset labels [default: false]', type=bool, metavar='BOOL')
  @click.option('--subset', help='Train with only N images [default: all]', type=int, metavar='INT')
  @click.option('--mirror', help='Enable dataset x-flips [default: false]', type=bool, metavar='BOOL')
  
  # Base config.
  @click.option('--cfg', help='Base config [default: auto]', type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar']))
  @click.option('--gamma', help='Override R1 gamma', type=float)
  @click.option('--kimg', help='Override training duration', type=int, metavar='INT')
  @click.option('--batch', help='Override batch size', type=int, metavar='INT')
  
  # Discriminator augmentation.
  @click.option('--aug', help='Augmentation mode [default: ada]', type=click.Choice(['noaug', 'ada', 'fixed']))
  @click.option('--p', help='Augmentation probability for --aug=fixed', type=float)
  @click.option('--target', help='ADA target value for --aug=ada', type=float)
  @click.option('--augpipe', help='Augmentation pipeline [default: bgc]', type=click.Choice(['blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc', 'bgcf', 'bgcfn', 'bgcfnc']))
  
  # Transfer learning.
  @click.option('--resume', help='Resume training [default: noresume]', metavar='PKL')
  @click.option('--freezed', help='Freeze-D [default: 0 layers]', type=int, metavar='INT')
  
  # Performance options.
  @click.option('--fp32', help='Disable mixed-precision training', type=bool, metavar='BOOL')
  @click.option('--nhwc', help='Use NHWC memory format with FP16', type=bool, metavar='BOOL')
  @click.option('--nobench', help='Disable cuDNN benchmarking', type=bool, metavar='BOOL')
  @click.option('--allow-tf32', help='Allow PyTorch to use TF32 internally', type=bool, metavar='BOOL')
  @click.option('--workers', help='Override number of DataLoader workers', type=int, metavar='INT')
  
  def main(ctx, outdir, dry_run, **config_kwargs):
      """Train a GAN using the techniques described in the paper
      "Training Generative Adversarial Networks with Limited Data".
  
      Examples:
  
      \b
      # Train with custom dataset using 1 GPU.
      python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1
  
      \b
      # Train class-conditional CIFAR-10 using 2 GPUs.
      python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \\
          --gpus=2 --cfg=cifar --cond=1
  
      \b
      # Transfer learn MetFaces from FFHQ using 4 GPUs.
      python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \\
          --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10
  
      \b
      # Reproduce original StyleGAN2 config F.
      python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \\
          --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug
  
      \b
      Base configs (--cfg):
        auto       Automatically select reasonable defaults based on resolution
                   and GPU count. Good starting point for new datasets.
        stylegan2  Reproduce results for StyleGAN2 config F at 1024x1024.
        paper256   Reproduce results for FFHQ and LSUN Cat at 256x256.
        paper512   Reproduce results for BreCaHAD and AFHQ at 512x512.
        paper1024  Reproduce results for MetFaces at 1024x1024.
        cifar      Reproduce results for CIFAR-10 at 32x32.
  
      \b
      Transfer learning source networks (--resume):
        ffhq256        FFHQ trained at 256x256 resolution.
        ffhq512        FFHQ trained at 512x512 resolution.
        ffhq1024       FFHQ trained at 1024x1024 resolution.
        celebahq256    CelebA-HQ trained at 256x256 resolution.
        lsundog256     LSUN Dog trained at 256x256 resolution.
        <PATH or URL>  Custom network pickle.
      """
      dnnlib.util.Logger(should_flush=True)
  
      # Setup training options.
      try:
          run_desc, args = setup_training_loop_kwargs(**config_kwargs)
      except UserError as err:
          ctx.fail(err)
  
      # Pick output directory.
      prev_run_dirs = []
      if os.path.isdir(outdir):
          prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
      prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
      prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
      cur_run_id = max(prev_run_ids, default=-1) + 1
      args.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{run_desc}')
      assert not os.path.exists(args.run_dir)
  
      # Print options.
      print()
      print('Training options:')
      print(json.dumps(args, indent=2))
      print()
      print(f'Output directory:   {args.run_dir}')
      print(f'Training data:      {args.training_set_kwargs.path}')
      print(f'Training duration:  {args.total_kimg} kimg')
      print(f'Number of GPUs:     {args.num_gpus}')
      print(f'Number of images:   {args.training_set_kwargs.max_size}')
      print(f'Image resolution:   {args.training_set_kwargs.resolution}')
      print(f'Conditional model:  {args.training_set_kwargs.use_labels}')
      print(f'Dataset x-flips:    {args.training_set_kwargs.xflip}')
      print(f'Discriminator use normalization:  {args.d_use_norm}')
      print(f'Discriminator use fts: {args.d_use_fts}')
  
      # Dry run?
      if dry_run:
          print('Dry run; exiting.')
          return
  
      # Create output directory.
      print('Creating output directory...')
      os.makedirs(args.run_dir)
      with open(os.path.join(args.run_dir, 'training_options.json'), 'wt') as f:
          json.dump(args, f, indent=2)
  
      # Launch processes.
      print('Launching processes...')
      torch.multiprocessing.set_start_method('spawn')
      with tempfile.TemporaryDirectory() as temp_dir:
          if args.num_gpus == 1:
              subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
          else:
              torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus)
  
  #----------------------------------------------------------------------------
  
  if __name__ == "__main__":
      main() # pylint: disable=no-value-for-parameter
  
  #----------------------------------------------------------------------------
  
apis: torch.distributed.init_process_group torch.multiprocessing.set_start_method torch.device torch.multiprocessing.spawn