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 _merge_subword_tokens(self, subword_outputs, word_starts):
      instances = []
      max_seq_length = subword_outputs.shape[1]
  
      # handling instance by instance
      for i in range(len(subword_outputs)):
          subword_vecs = subword_outputs[i]
          word_vecs = []
          starts = word_starts[i]
          mask = starts.ne(self.model_config.pad_token_id)
          starts = starts[mask]
          for j in range(len(starts) - 1):
              if starts[j + 1] <= 0:
                  break
  
              start = starts[j]
              end = starts[j + 1]
              vecs_range = subword_vecs[start:end]
              word_vecs.append(torch.mean(vecs_range, 0).unsqueeze(0))
  
          instances.append(word_vecs)
  
      t_insts = []
      zero_tens = torch.zeros(self.model_config.hidden_size).unsqueeze(
          0
      )
      zero_tens = zero_tens.to(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
  
      for inst in instances:
          if len(inst) < max_seq_length:
              for i in range(max_seq_length - len(inst)):
                  inst.append(zero_tens)
          t_insts.append(torch.cat(inst, dim=0).unsqueeze(0))
  
      w_tens = torch.cat(t_insts, dim=0)
      return w_tens
  
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 4 for calling the BiaffineParsingHead obj's underlying member method code with example usage: obj._merge_subword_tokens(subword_outputs, word_starts) and returns: w_tens