class_docstr: Credit: G. Glavaš & I. Vulić Based on paper "Is Supervised Syntactic Parsing Beneficial for Language Understanding?
  An Empirical Investigation" (https://arxiv.org/pdf/2008.06788.pdf)
code: def forward(
      self,
      outputs,
      cls_output=None,
      attention_mask=None,
      return_dict=False,
      word_starts=None,
      labels_arcs=None,
      labels_rels=None,
      **kwargs,
  ):
      outs = self.dropout(outputs[0])
      word_outputs_deps = self._merge_subword_tokens(outs, word_starts)
  
      # adding the CLS representation as the representation for the "root" parse token
      # cls_output = self.pooler_activation(self.pooler_dense(outs[:, 0]))
      cls_output = outs[:, 0]
      word_outputs_heads = torch.cat(
          [cls_output.unsqueeze(1), word_outputs_deps], dim=1
      )
  
      arc_preds = self.biaffine_arcs(
          word_outputs_deps, word_outputs_heads
      )
      arc_preds = arc_preds.squeeze()
      if len(arc_preds.shape) == 2:
          arc_preds = arc_preds.unsqueeze(0)
  
      rel_preds = self.biaffine_rels(
          word_outputs_deps, word_outputs_heads
      )
      rel_preds = rel_preds.permute(0, 2, 3, 1)
  
      loss = self._get_loss(
          arc_preds, rel_preds, labels_arcs, labels_rels, self.loss_fn
      )
  
      if return_dict:
          return DependencyParsingOutput(
              loss=loss,
              rel_preds=rel_preds,
              arc_preds=arc_preds,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
      else:
          outputs = (rel_preds, arc_preds)
          if loss is not None:
              outputs = (loss,) + outputs
          return outputs
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers.utils import ModelOutput
  from .base import PredictionHead
label_desc: Use image node_id 3 for calling the BiaffineParsingHead obj's underlying member method code with example usage: obj.forward(outputs, cls_output, attention_mask, return_dict, word_starts, labels_arcs, labels_rels) and returns: DependencyParsingOutput, outputs