class_docstr: KEModel for DataParallel mode.
code: def forward(
      self,
      h,
      r,
      t,
      all_ents,
      neg_ents,
      all_ents_emb,
      rel_emb,
      mode,
      weights,
  ):
      h_emb, r_emb, t_emb, neg_emb, mask = self.model.prepare_inputs(
          h,
          r,
          t,
          all_ents,
          neg_ents,
          all_ents_emb,
          rel_emb,
          mode,
          self.args,
      )
      pos_score = self.model.forward(h_emb, r_emb, t_emb)
  
      if mode == "head":
          neg_score = self.model.get_neg_score(
              t_emb, r_emb, neg_emb, True, mask
          )
      elif mode == "tail":
          neg_score = self.model.get_neg_score(
              h_emb, r_emb, neg_emb, False, mask
          )
      else:
          raise ValueError("Unsupported negative mode {}.".format(mode))
      neg_score = neg_score.reshape([self.args.batch_size, -1])
  
      loss = self.loss_func(pos_score, neg_score, weights)
      if self.args.use_embedding_regularization:
          reg_loss = self.model.get_regularization(
              h_emb, r_emb, t_emb, neg_emb
          )
  
          loss = loss + reg_loss
      return loss
  
imports: import os
  import sys
  import time
  import warnings
  from collections import defaultdict
  import paddle
  import numpy
  import paddle.nn
  import paddle.distributed
  from paddle.optimizer.lr import StepDecay
  from dataset.reader import read_trigraph
  from dataset.dataset import create_dataloaders
  from models.ke_model import KGEModel
  from models.loss_func import LossFunction
  from utils import set_seed, set_logger, print_log
  from utils import evaluate
  from config import prepare_config
label_desc: Use image node_id 2 for calling the KEModelDP obj's underlying member method code with example usage: obj.forward(h, r, t, all_ents, neg_ents, all_ents_emb, rel_emb, mode, weights) and returns: loss