code: def __init__(self, dim_in, dim_out):
      super(GRUEmbedding, self).__init__()
      self.Wx0 = nn.Embedding(dim_in, dim_out * 3, sparse=True)
      self.Wrz0 = nn.Parameter(
          torch.empty((dim_out, dim_out * 2), dtype=torch.float)
      )
      self.Wh0 = nn.Parameter(
          torch.empty((dim_out, dim_out * 1), dtype=torch.float)
      )
      self.Bh0 = nn.Parameter(
          torch.zeros(dim_out * 3, dtype=torch.float)
      )
      self.reset_parameters()
  
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 1 to create a new GRUEmbedding object from inherited base classes: nn with example: obj = GRUEmbedding(dim_in, dim_out)