code: def forward(
      self,
      input_ids,
      token_type_ids=None,
      attention_mask=None,
      valid_ids=None,
  ):
      sequence_output = self.bert(
          input_ids=input_ids,
          token_type_ids=token_type_ids,
          attention_mask=attention_mask,
          head_mask=None,
      )[0]
      batch_size, max_len, feat_dim = sequence_output.shape
      valid_output = torch.zeros(
          batch_size,
          max_len,
          feat_dim,
          dtype=torch.float32,
          device="cuda" if torch.cuda.is_available() else "cpu",
      )
      for i in range(batch_size):
          jj = -1
          for j in range(max_len):
              if valid_ids[i][j].item() == 1:
                  jj += 1
                  valid_output[i][jj] = sequence_output[i][j]
      sequence_output = self.dropout(valid_output)
      logits = self.classifier(sequence_output)
      return logits
  
file_path: DeepKE/src/deepke/name_entity_re/standard/models/InferBert.py
imports: import json
  import os
  import torch
  import torch.nn.functional
  from transformers import BertConfig, BertForTokenClassification, BertTokenizer
  from collections import OrderedDict
  from .BiLSTM_CRF import *
  import hydra
  from hydra import utils
  import nltk
  from nltk import word_tokenize
label_desc: Use image node_id 1 for calling the BertNer obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, attention_mask, valid_ids) and returns: logits