text: from transformers import RobertaForSequenceClassification
  
  
  class RecconEmotionEntailmentModel(RobertaForSequenceClassification):
      """The Reccon Emotion Entailment Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
  
      This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
      general usage and behavior.
  
      Args:
          config (:class:`~reccon.RecconEmotionEntailmentConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not
              load the weights associated with the model, only the configuration. Use the :obj:`.from_pretrained` method to load the model weights.
  
      Example::
  
          from sgnlp.models.emotion_entailment import RecconEmotionEntailmentConfig, RecconEmotionEntailmentModel, RecconEmotionEntailmentTokenizer
  
          # 1. load from default
          config = RecconEmotionEntailmentConfig()
          model = RecconEmotionEntailmentModel(config)
          # 2. load from pretrained
          config = RecconEmotionEntailmentConfig.from_pretrained("https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/config.json")
          model = RecconEmotionEntailmentModel.from_pretrained("https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/pytorch_model.bin", config=config)
  
          # Using model
          tokenizer = RecconEmotionEntailmentTokenizer.from_pretrained("roberta-base")
          text = "surprise <SEP> Me ? You're the one who pulled out in front of me ! <SEP> Why don't you watch where you're going ? <SEP> Why don't you watch where you're going ? Me ? You're the one who pulled out in front of me !"
          inputs = tokenizer(text, return_tensors="pt")
          outputs = model(**inputs)
  
      """
  
      def __init__(self, config):
          super().__init__(config)
  
      def forward(self, **kwargs):
          return super().forward(**kwargs)
  