text: import torch
  
  from multiprocessing import cpu_count
  
  from simpletransformers.classification import ClassificationModel
  from simpletransformers.custom_models.models import (
      BertForMultiLabelSequenceClassification,
      RobertaForMultiLabelSequenceClassification,
      XLNetForMultiLabelSequenceClassification,
      XLMForMultiLabelSequenceClassification,
      DistilBertForMultiLabelSequenceClassification,
      AlbertForMultiLabelSequenceClassification,
  )
  from transformers import (
      WEIGHTS_NAME,
      BertConfig,
      BertTokenizer,
      XLNetConfig,
      XLNetTokenizer,
      XLMConfig,
      XLMTokenizer,
      RobertaConfig,
      RobertaTokenizer,
      DistilBertConfig,
      DistilBertTokenizer,
      AlbertConfig,
      AlbertTokenizer,
  )
  
  
  class MultiLabelClassificationModel(ClassificationModel):
      def __init__(self, model_type, model_name, num_labels=None, pos_weight=None, args=None, use_cuda=True):
          """
          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.
          """
          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),
          }
  
          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.num_labels = num_labels
          else:
              self.config = config_class.from_pretrained(model_name)
              self.num_labels = self.config.num_labels
          self.tokenizer = tokenizer_class.from_pretrained(model_name)
          self.tokenizer = tokenizer_class.from_pretrained(model_name)
          self.num_labels = num_labels
          self.pos_weight = pos_weight
          self.sliding_window = False
  
          if use_cuda:
              if torch.cuda.is_available():
                  self.device = torch.device("cuda")
              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)
              )
          else:
              self.model = model_class.from_pretrained(model_name, config=self.config)
  
          self.results = {}
  
          self.args = {
              "output_dir": "outputs/",
              "cache_dir": "cache_dir/",
              "fp16": False,
              "fp16_opt_level": "O1",
              "max_seq_length": 128,
              "train_batch_size": 8,
              "gradient_accumulation_steps": 1,
              "eval_batch_size": 8,
              "num_train_epochs": 1,
              "weight_decay": 0,
              "learning_rate": 4e-5,
              "adam_epsilon": 1e-8,
              "warmup_ratio": 0.06,
              "warmup_steps": 0,
              "max_grad_norm": 1.0,
              "stride": False,
              "logging_steps": 50,
              "save_steps": 2000,
              "evaluate_during_training": False,
              "overwrite_output_dir": False,
              "reprocess_input_data": False,
              "process_count": cpu_count() - 2 if cpu_count() > 2 else 1,
              "n_gpu": 1,
              "use_multiprocessing": True,
              "silent": False,
              "threshold": 0.5,
          }
  
          if not use_cuda:
              self.args["fp16"] = False
  
          if args:
              self.args.update(args)
  
          self.args["model_name"] = model_name
          self.args["model_type"] = model_type
  
      def train_model(
          self, train_df, multi_label=True, eval_df=None, output_dir=None, show_running_loss=True, args=None
      ):
          return super().train_model(
              train_df,
              multi_label=multi_label,
              eval_df=eval_df,
              output_dir=output_dir,
              show_running_loss=show_running_loss,
              args=args,
          )
  
      def eval_model(self, eval_df, multi_label=True, output_dir=None, verbose=False, **kwargs):
          return super().eval_model(eval_df, output_dir=output_dir, multi_label=multi_label, verbose=verbose, **kwargs)
  
      def evaluate(self, eval_df, output_dir, multi_label=True, prefix="", **kwargs):
          return super().evaluate(eval_df, output_dir, multi_label=multi_label, prefix=prefix, **kwargs)
  
      def load_and_cache_examples(self, examples, evaluate=False, no_cache=False, multi_label=True):
          return super().load_and_cache_examples(examples, evaluate=evaluate, no_cache=no_cache, multi_label=multi_label)
  
      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)
  