code: import logging
  import random
  import warnings
  from multiprocessing import cpu_count
  
  import numpy as np
  import torch
  from transformers import (
      WEIGHTS_NAME,
      AlbertConfig,
      AlbertTokenizer,
      BertConfig,
      BertTokenizer,
      DistilBertConfig,
      DistilBertTokenizer,
      ElectraConfig,
      ElectraTokenizer,
      FlaubertConfig,
      FlaubertTokenizer,
      RobertaConfig,
      RobertaTokenizer,
      XLMConfig,
      XLMRobertaConfig,
      XLMRobertaTokenizer,
      XLMTokenizer,
      XLNetConfig,
      XLNetTokenizer,
  )
  
  from simpletransformers.classification import ClassificationModel
  from simpletransformers.config.global_args import global_args
  from simpletransformers.config.model_args import MultiLabelClassificationArgs
  from simpletransformers.custom_models.models import (
      AlbertForMultiLabelSequenceClassification,
      BertForMultiLabelSequenceClassification,
      DistilBertForMultiLabelSequenceClassification,
      ElectraForMultiLabelSequenceClassification,
      FlaubertForMultiLabelSequenceClassification,
      RobertaForMultiLabelSequenceClassification,
      XLMForMultiLabelSequenceClassification,
      XLMRobertaForMultiLabelSequenceClassification,
      XLNetForMultiLabelSequenceClassification,
  )
  
  try:
      import wandb
  
      wandb_available = True
  except ImportError:
      wandb_available = False
  
  logger = logging.getLogger(__name__)
  
  
  class MultiLabelClassificationModel(ClassificationModel):
      def __init__(
          self,
          model_type,
          model_name,
          num_labels=None,
          pos_weight=None,
          args=None,
          use_cuda=True,
          cuda_device=-1,
          **kwargs,
      ):
  
          """
          Initializes a MultiLabelClassification model.
  
          Args:
              model_type: The type of model (bert, roberta)
              model_name: Default Transformer model name or path to a directory containing Transformer model file (pytorch_nodel.bin).
              num_labels (optional): The number of labels or classes in the dataset.
              pos_weight (optional): A list of length num_labels containing the weights to assign to each label for loss calculation.
              args (optional): Default args will be used if this parameter is not provided. If provided, it should be a dict containing the args that should be changed in the default args.
              use_cuda (optional): Use GPU if available. Setting to False will force model to use CPU only.
              cuda_device (optional): Specific GPU that should be used. Will use the first available GPU by default.
              **kwargs (optional): For providing proxies, force_download, resume_download, cache_dir and other options specific to the 'from_pretrained' implementation where this will be supplied.
          """  # noqa: ignore flake8"
  
          MODEL_CLASSES = {
              "bert": (BertConfig, BertForMultiLabelSequenceClassification, BertTokenizer,),
              "roberta": (RobertaConfig, RobertaForMultiLabelSequenceClassification, RobertaTokenizer,),
              "xlnet": (XLNetConfig, XLNetForMultiLabelSequenceClassification, XLNetTokenizer,),
              "xlm": (XLMConfig, XLMForMultiLabelSequenceClassification, XLMTokenizer),
              "distilbert": (DistilBertConfig, DistilBertForMultiLabelSequenceClassification, DistilBertTokenizer,),
              "albert": (AlbertConfig, AlbertForMultiLabelSequenceClassification, AlbertTokenizer,),
              "flaubert": (FlaubertConfig, FlaubertForMultiLabelSequenceClassification, FlaubertTokenizer,),
              "xlmroberta": (XLMRobertaConfig, XLMRobertaForMultiLabelSequenceClassification, XLMRobertaTokenizer,),
              "electra": (ElectraConfig, ElectraForMultiLabelSequenceClassification, ElectraTokenizer),
          }
  
          self.args = self._load_model_args(model_name)
  
          if isinstance(args, dict):
              self.args.update_from_dict(args)
          elif isinstance(args, MultiLabelClassificationArgs):
              self.args = args
  
          if "sweep_config" in kwargs:
              sweep_config = kwargs.pop("sweep_config")
              sweep_values = {key: value["value"] for key, value in sweep_config.as_dict().items() if key != "_wandb"}
              self.args.update_from_dict(sweep_values)
  
          if self.args.manual_seed:
              random.seed(self.args.manual_seed)
              np.random.seed(self.args.manual_seed)
              torch.manual_seed(self.args.manual_seed)
              if self.args.n_gpu > 0:
                  torch.cuda.manual_seed_all(self.args.manual_seed)
  
          if not use_cuda:
              self.args.fp16 = False
  
          config_class, model_class, tokenizer_class = MODEL_CLASSES[model_type]
          if num_labels:
              self.config = config_class.from_pretrained(model_name, num_labels=num_labels, **self.args.config)
              self.num_labels = num_labels
          else:
              self.config = config_class.from_pretrained(model_name, **self.args.config)
              self.num_labels = self.config.num_labels
          self.pos_weight = pos_weight
  
          if use_cuda:
              if torch.cuda.is_available():
                  if cuda_device == -1:
                      self.device = torch.device("cuda")
                  else:
                      self.device = torch.device(f"cuda:{cuda_device}")
              else:
                  raise ValueError(
                      "'use_cuda' set to True when cuda is unavailable."
                      " Make sure CUDA is available or set use_cuda=False."
                  )
          else:
              self.device = "cpu"
  
          if self.pos_weight:
              self.model = model_class.from_pretrained(
                  model_name, config=self.config, pos_weight=torch.Tensor(self.pos_weight).to(self.device), **kwargs
              )
          else:
              self.model = model_class.from_pretrained(model_name, config=self.config, **kwargs)
  
          self.results = {}
  
          self.tokenizer = tokenizer_class.from_pretrained(model_name, do_lower_case=self.args.do_lower_case, **kwargs)
  
          self.args.model_name = model_name
          self.args.model_type = model_type
  
          if self.args.wandb_project and not wandb_available:
              warnings.warn("wandb_project specified but wandb is not available. Wandb disabled.")
              self.args.wandb_project = None
  
      def _load_model_args(self, input_dir):
          args = MultiLabelClassificationArgs()
          args.load(input_dir)
          return args
  
      def train_model(
          self,
          train_df,
          multi_label=True,
          eval_df=None,
          output_dir=None,
          show_running_loss=True,
          args=None,
          verbose=True,
          **kwargs,
      ):
          return super().train_model(
              train_df,
              multi_label=multi_label,
              eval_df=eval_df,
              output_dir=output_dir,
              show_running_loss=show_running_loss,
              verbose=True,
              args=args,
              **kwargs,
          )
  
      def eval_model(self, eval_df, multi_label=True, output_dir=None, verbose=False, silent=False, **kwargs):
          return super().eval_model(
              eval_df, output_dir=output_dir, multi_label=multi_label, verbose=verbose, silent=silent, **kwargs
          )
  
      def evaluate(self, eval_df, output_dir, multi_label=True, prefix="", verbose=True, silent=False, **kwargs):
          return super().evaluate(
              eval_df, output_dir, multi_label=multi_label, prefix=prefix, verbose=verbose, silent=silent, **kwargs
          )
  
      def load_and_cache_examples(
          self, examples, evaluate=False, no_cache=False, multi_label=True, verbose=True, silent=False
      ):
          return super().load_and_cache_examples(
              examples, evaluate=evaluate, no_cache=no_cache, multi_label=multi_label, verbose=verbose, silent=silent
          )
  
      def compute_metrics(self, preds, labels, eval_examples, multi_label=True, **kwargs):
          return super().compute_metrics(preds, labels, eval_examples, multi_label=multi_label, **kwargs)
  
      def predict(self, to_predict, multi_label=True):
          return super().predict(to_predict, multi_label=multi_label)
  
apis: torch.cuda.manual_seed_all torch.manual_seed numpy.random.seed torch.cuda.is_available torch.device torch.Tensor