code: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
  
  from __future__ import absolute_import, division, print_function
  
  import argparse
  import glob
  import logging
  import os
  import random
  
  import numpy as np
  import torch
  from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                                TensorDataset)
  from torch.utils.data.distributed import DistributedSampler
  
  try:
      from torch.utils.tensorboard import SummaryWriter
  except:
      from tensorboardX import SummaryWriter
  
  from tqdm import tqdm, trange
  
  from transformers import (WEIGHTS_NAME, BertConfig,
                                    BertForSequenceClassification, BertTokenizer,
                                    RobertaConfig,
                                    RobertaForSequenceClassification,
                                    RobertaTokenizer,
                                    XLMConfig, XLMForSequenceClassification,
                                    XLMTokenizer, XLNetConfig,
                                    XLNetForSequenceClassification,
                                    XLNetTokenizer,
                                    DistilBertConfig,
                                    DistilBertForSequenceClassification,
                                    DistilBertTokenizer)
  
  from transformers import AdamW, WarmupLinearSchedule
  
  from transformers import glue_compute_metrics as compute_metrics
  from transformers import glue_output_modes as output_modes
  from transformers import glue_processors as processors
  from transformers import glue_convert_examples_to_features as convert_examples_to_features
  
  logger = logging.getLogger(__name__)
  
  ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig, 
                                                                                  RobertaConfig, DistilBertConfig)), ())
  
  MODEL_CLASSES = {
      'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
      'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
      'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
      'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
      'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
  }
  
  
  def set_seed(args):
      random.seed(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)
  
  
  def train(args, train_dataset, model, tokenizer):
      """ Train the model """
      if args.local_rank in [-1, 0]:
          tb_writer = SummaryWriter()
  
      args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
      train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
      train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
  
      if args.max_steps > 0:
          t_total = args.max_steps
          args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
      else:
          t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
  
      # Prepare optimizer and schedule (linear warmup and decay)
      no_decay = ['bias', 'LayerNorm.weight']
      optimizer_grouped_parameters = [
          {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
          {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
          ]
      optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
      scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
      if args.fp16:
          try:
              from apex import amp
          except ImportError:
              raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
          model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
  
      # multi-gpu training (should be after apex fp16 initialization)
      if args.n_gpu > 1:
          model = torch.nn.DataParallel(model)
  
      # Distributed training (should be after apex fp16 initialization)
      if args.local_rank != -1:
          model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
                                                            output_device=args.local_rank,
                                                            find_unused_parameters=True)
  
      # Train!
      logger.info("***** Running training *****")
      logger.info("  Num examples = %d", len(train_dataset))
      logger.info("  Num Epochs = %d", args.num_train_epochs)
      logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
      logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
                     args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
      logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
      logger.info("  Total optimization steps = %d", t_total)
  
      global_step = 0
      tr_loss, logging_loss = 0.0, 0.0
      model.zero_grad()
      train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
      set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
      for _ in train_iterator:
          epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
          for step, batch in enumerate(epoch_iterator):
              model.train()
              batch = tuple(t.to(args.device) for t in batch)
              inputs = {'input_ids':      batch[0],
                        'attention_mask': batch[1],
                        'labels':         batch[3]}
              if args.model_type != 'distilbert':
                  inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None  # XLM, DistilBERT and RoBERTa don't use segment_ids
              outputs = model(**inputs)
              loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
  
              if args.n_gpu > 1:
                  loss = loss.mean() # mean() to average on multi-gpu parallel training
              if args.gradient_accumulation_steps > 1:
                  loss = loss / args.gradient_accumulation_steps
  
              if args.fp16:
                  with amp.scale_loss(loss, optimizer) as scaled_loss:
                      scaled_loss.backward()
                  torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
              else:
                  loss.backward()
                  torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
  
              tr_loss += loss.item()
              if (step + 1) % args.gradient_accumulation_steps == 0:
                  optimizer.step()
                  scheduler.step()  # Update learning rate schedule
                  model.zero_grad()
                  global_step += 1
  
                  if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                      # Log metrics
                      if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
                          results = evaluate(args, model, tokenizer)
                          for key, value in results.items():
                              tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
                      tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
                      tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                      logging_loss = tr_loss
  
                  if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                      # Save model checkpoint
                      output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
                      if not os.path.exists(output_dir):
                          os.makedirs(output_dir)
                      model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                      model_to_save.save_pretrained(output_dir)
                      torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                      logger.info("Saving model checkpoint to %s", output_dir)
  
              if args.max_steps > 0 and global_step > args.max_steps:
                  epoch_iterator.close()
                  break
          if args.max_steps > 0 and global_step > args.max_steps:
              train_iterator.close()
              break
  
      if args.local_rank in [-1, 0]:
          tb_writer.close()
  
      return global_step, tr_loss / global_step
  
  
  def evaluate(args, model, tokenizer, prefix=""):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
      eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)
  
      results = {}
      for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
          eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
  
          if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
              os.makedirs(eval_output_dir)
  
          args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
          # Note that DistributedSampler samples randomly
          eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
          eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
  
          # Eval!
          logger.info("***** Running evaluation {} *****".format(prefix))
          logger.info("  Num examples = %d", len(eval_dataset))
          logger.info("  Batch size = %d", args.eval_batch_size)
          eval_loss = 0.0
          nb_eval_steps = 0
          preds = None
          out_label_ids = None
          for batch in tqdm(eval_dataloader, desc="Evaluating"):
              model.eval()
              batch = tuple(t.to(args.device) for t in batch)
  
              with torch.no_grad():
                  inputs = {'input_ids':      batch[0],
                            'attention_mask': batch[1],
                            'labels':         batch[3]}
                  if args.model_type != 'distilbert':
                      inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None  # XLM, DistilBERT and RoBERTa don't use segment_ids
                  outputs = model(**inputs)
                  tmp_eval_loss, logits = outputs[:2]
  
                  eval_loss += tmp_eval_loss.mean().item()
              nb_eval_steps += 1
              if preds is None:
                  preds = logits.detach().cpu().numpy()
                  out_label_ids = inputs['labels'].detach().cpu().numpy()
              else:
                  preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                  out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)
  
          eval_loss = eval_loss / nb_eval_steps
          if args.output_mode == "classification":
              preds = np.argmax(preds, axis=1)
          elif args.output_mode == "regression":
              preds = np.squeeze(preds)
          result = compute_metrics(eval_task, preds, out_label_ids)
          results.update(result)
  
          output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
          with open(output_eval_file, "w") as writer:
              logger.info("***** Eval results {} *****".format(prefix))
              for key in sorted(result.keys()):
                  logger.info("  %s = %s", key, str(result[key]))
                  writer.write("%s = %s\n" % (key, str(result[key])))
  
      return results
  
  
  def load_and_cache_examples(args, task, tokenizer, evaluate=False):
      if args.local_rank not in [-1, 0] and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      processor = processors[task]()
      output_mode = output_modes[task]
      # Load data features from cache or dataset file
      cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format(
          'dev' if evaluate else 'train',
          list(filter(None, args.model_name_or_path.split('/'))).pop(),
          str(args.max_seq_length),
          str(task)))
      if os.path.exists(cached_features_file) and not args.overwrite_cache:
          logger.info("Loading features from cached file %s", cached_features_file)
          features = torch.load(cached_features_file)
      else:
          logger.info("Creating features from dataset file at %s", args.data_dir)
          label_list = processor.get_labels()
          if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']:
              # HACK(label indices are swapped in RoBERTa pretrained model)
              label_list[1], label_list[2] = label_list[2], label_list[1] 
          examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
          features = convert_examples_to_features(examples,
                                                  tokenizer,
                                                  label_list=label_list,
                                                  max_length=args.max_seq_length,
                                                  output_mode=output_mode,
                                                  pad_on_left=bool(args.model_type in ['xlnet']),                 # pad on the left for xlnet
                                                  pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                                                  pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
          )
          if args.local_rank in [-1, 0]:
              logger.info("Saving features into cached file %s", cached_features_file)
              torch.save(features, cached_features_file)
  
      if args.local_rank == 0 and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      # Convert to Tensors and build dataset
      all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
      all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
      all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
      if output_mode == "classification":
          all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
      elif output_mode == "regression":
          all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
  
      dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
      return dataset
  
  
  def main():
      parser = argparse.ArgumentParser()
  
      ## Required parameters
      parser.add_argument("--data_dir", default=None, type=str, required=True,
                          help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
      parser.add_argument("--model_type", default=None, type=str, required=True,
                          help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
      parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
                          help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
      parser.add_argument("--task_name", default=None, type=str, required=True,
                          help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
      parser.add_argument("--output_dir", default=None, type=str, required=True,
                          help="The output directory where the model predictions and checkpoints will be written.")
  
      ## Other parameters
      parser.add_argument("--config_name", default="", type=str,
                          help="Pretrained config name or path if not the same as model_name")
      parser.add_argument("--tokenizer_name", default="", type=str,
                          help="Pretrained tokenizer name or path if not the same as model_name")
      parser.add_argument("--cache_dir", default="", type=str,
                          help="Where do you want to store the pre-trained models downloaded from s3")
      parser.add_argument("--max_seq_length", default=128, type=int,
                          help="The maximum total input sequence length after tokenization. Sequences longer "
                               "than this will be truncated, sequences shorter will be padded.")
      parser.add_argument("--do_train", action='store_true',
                          help="Whether to run training.")
      parser.add_argument("--do_eval", action='store_true',
                          help="Whether to run eval on the dev set.")
      parser.add_argument("--evaluate_during_training", action='store_true',
                          help="Rul evaluation during training at each logging step.")
      parser.add_argument("--do_lower_case", action='store_true',
                          help="Set this flag if you are using an uncased model.")
  
      parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
                          help="Batch size per GPU/CPU for training.")
      parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
                          help="Batch size per GPU/CPU for evaluation.")
      parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                          help="Number of updates steps to accumulate before performing a backward/update pass.")
      parser.add_argument("--learning_rate", default=5e-5, type=float,
                          help="The initial learning rate for Adam.")
      parser.add_argument("--weight_decay", default=0.0, type=float,
                          help="Weight deay if we apply some.")
      parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                          help="Epsilon for Adam optimizer.")
      parser.add_argument("--max_grad_norm", default=1.0, type=float,
                          help="Max gradient norm.")
      parser.add_argument("--num_train_epochs", default=3.0, type=float,
                          help="Total number of training epochs to perform.")
      parser.add_argument("--max_steps", default=-1, type=int,
                          help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
      parser.add_argument("--warmup_steps", default=0, type=int,
                          help="Linear warmup over warmup_steps.")
  
      parser.add_argument('--logging_steps', type=int, default=50,
                          help="Log every X updates steps.")
      parser.add_argument('--save_steps', type=int, default=50,
                          help="Save checkpoint every X updates steps.")
      parser.add_argument("--eval_all_checkpoints", action='store_true',
                          help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
      parser.add_argument("--no_cuda", action='store_true',
                          help="Avoid using CUDA when available")
      parser.add_argument('--overwrite_output_dir', action='store_true',
                          help="Overwrite the content of the output directory")
      parser.add_argument('--overwrite_cache', action='store_true',
                          help="Overwrite the cached training and evaluation sets")
      parser.add_argument('--seed', type=int, default=42,
                          help="random seed for initialization")
  
      parser.add_argument('--fp16', action='store_true',
                          help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
      parser.add_argument('--fp16_opt_level', type=str, default='O1',
                          help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                               "See details at https://nvidia.github.io/apex/amp.html")
      parser.add_argument("--local_rank", type=int, default=-1,
                          help="For distributed training: local_rank")
      parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
      parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
      args = parser.parse_args()
  
      if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
          raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
  
      # Setup distant debugging if needed
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
          print("Waiting for debugger attach")
          ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
          ptvsd.wait_for_attach()
  
      # Setup CUDA, GPU & distributed training
      if args.local_rank == -1 or args.no_cuda:
          device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
          args.n_gpu = torch.cuda.device_count()
      else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.cuda.set_device(args.local_rank)
          device = torch.device("cuda", args.local_rank)
          torch.distributed.init_process_group(backend='nccl')
          args.n_gpu = 1
      args.device = device
  
      # Setup logging
      logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                          datefmt = '%m/%d/%Y %H:%M:%S',
                          level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
      logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
                      args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
  
      # Set seed
      set_seed(args)
  
      # Prepare GLUE task
      args.task_name = args.task_name.lower()
      if args.task_name not in processors:
          raise ValueError("Task not found: %s" % (args.task_name))
      processor = processors[args.task_name]()
      args.output_mode = output_modes[args.task_name]
      label_list = processor.get_labels()
      num_labels = len(label_list)
  
      # Load pretrained model and tokenizer
      if args.local_rank not in [-1, 0]:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
  
      args.model_type = args.model_type.lower()
      config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
      config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
      tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
      model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
  
      if args.local_rank == 0:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
  
      model.to(args.device)
  
      logger.info("Training/evaluation parameters %s", args)
  
  
      # Training
      if args.do_train:
          train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
          global_step, tr_loss = train(args, train_dataset, model, tokenizer)
          logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
  
  
      # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
      if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
          # Create output directory if needed
          if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
              os.makedirs(args.output_dir)
  
          logger.info("Saving model checkpoint to %s", args.output_dir)
          # Save a trained model, configuration and tokenizer using `save_pretrained()`.
          # They can then be reloaded using `from_pretrained()`
          model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
          model_to_save.save_pretrained(args.output_dir)
          tokenizer.save_pretrained(args.output_dir)
  
          # Good practice: save your training arguments together with the trained model
          torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
  
          # Load a trained model and vocabulary that you have fine-tuned
          model = model_class.from_pretrained(args.output_dir)
          tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
          model.to(args.device)
  
  
      # Evaluation
      results = {}
      if args.do_eval and args.local_rank in [-1, 0]:
          tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
          checkpoints = [args.output_dir]
          if args.eval_all_checkpoints:
              checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
              logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
          logger.info("Evaluate the following checkpoints: %s", checkpoints)
          for checkpoint in checkpoints:
              global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
              prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
              
              model = model_class.from_pretrained(checkpoint)
              model.to(args.device)
              result = evaluate(args, model, tokenizer, prefix=prefix)
              result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
              results.update(result)
  
      return results
  
  
  if __name__ == "__main__":
      main()
  
apis: torch.utils.data.DataLoader torch.cuda.manual_seed_all torch.no_grad numpy.random.seed torch.cuda.is_available torch.distributed.init_process_group torch.save torch.cuda.device_count torch.nn.DataParallel torch.utils.data.RandomSampler torch.device torch.cuda.set_device torch.load torch.distributed.get_world_size torch.distributed.get_rank torch.manual_seed torch.utils.data.SequentialSampler torch.tensor numpy.argmax torch.distributed.barrier torch.nn.parallel.DistributedDataParallel torch.utils.data.TensorDataset torch.utils.data.distributed.DistributedSampler numpy.squeeze