code: def forward(self, X, H, Y, training=False):
      E, O, B = self.embed(X, H, Y)
      if training:
          E = self.DE(E)
      if not (self.constrained_embedding or self.embedding):
          H[0] = E
      Xh = self.hidden_step(E, H, training=training)
      R = self.score_items(Xh, O, B)
      return R
  
imports: from collections import Counter
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Variable
  from torch.optim import Optimizer
  from cornac.utils.common import get_rng
label_desc: Use image node_id 16 for calling the GRU4RecModel obj's underlying member method code with example usage: obj.forward(X, H, Y, training) and returns: R