code: def forward(self, pixel_values, labels):
      # Predict
      outputs = self.vit(pixel_values=pixel_values)
  
      # Find the corresponding label
      output = self.dropout(outputs.last_hidden_state[:, 0])
      logits = self.classifier(output)
  
      # Compute loss
      loss = None
      if labels is not None:
          loss_fct = nn.CrossEntropyLoss()
          loss = loss_fct(
              logits.view(-1, self.num_labels), labels.view(-1)
          )
  
      # Return forward results
      return SequenceClassifierOutput(
          loss=loss,
          logits=logits,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
imports: import torch.nn
  from transformers import ViTModel
  from transformers.modeling_outputs import SequenceClassifierOutput
label_desc: Use image node_id 2 for calling the ViTForImageClassification obj's underlying member method code with example usage: obj.forward(pixel_values, labels) and returns: SequenceClassifierOutput