code: def __init__(self, model):
      super(ViTForImageClassification, self).__init__()
  
      # Load the huggingface model
      self.vit = model
      # Drop 10 % of the solutions
      self.dropout = nn.Dropout(0.1)
      # Adapt the softmax layer to the model
      self.classifier = nn.Linear(
          self.vit.config.hidden_size, len(self.vit.config.id2label)
      )
      # The size of the softmax layer
      self.num_labels = len(self.vit.config.id2label)
  
imports: import torch.nn
  from transformers import ViTModel
  from transformers.modeling_outputs import SequenceClassifierOutput
label_desc: Use image node_id 1 to create a new ViTForImageClassification object from inherited base classes: nn with example: obj = ViTForImageClassification(model)