code: def __init__(
      self,
      n_items,
      P0=None,
      layers=[100],
      dropout_p_embed=0.0,
      dropout_p_hidden=0.0,
      embedding=0,
      constrained_embedding=True,
      logq=0.0,
      sample_alpha=0.5,
      bpreg=1.0,
      elu_param=0.5,
      loss="cross-entropy",
  ):
      super(GRU4RecModel, self).__init__()
      self.n_items = n_items
      self.P0 = P0
      self.layers = layers
      self.dropout_p_embed = dropout_p_embed
      self.dropout_p_hidden = dropout_p_hidden
      self.embedding = embedding
      self.constrained_embedding = constrained_embedding
      self.logq = logq
      self.sample_alpha = sample_alpha
      self.elu_param = elu_param
      self.bpreg = bpreg
      self.loss = loss
      self.set_loss_function(self.loss)
      self.start = 0
      if constrained_embedding:
          n_input = layers[-1]
      elif embedding:
          self.E = nn.Embedding(n_items, embedding, sparse=True)
          n_input = embedding
      else:
          self.GE = GRUEmbedding(n_items, layers[0])
          n_input = n_items
          self.start = 1
      self.DE = nn.Dropout(dropout_p_embed)
      self.G = []
      self.D = []
      for i in range(self.start, len(layers)):
          self.G.append(
              nn.GRUCell(layers[i - 1] if i > 0 else n_input, layers[i])
          )
          self.D.append(nn.Dropout(dropout_p_hidden))
      self.G = nn.ModuleList(self.G)
      self.D = nn.ModuleList(self.D)
      self.Wy = nn.Embedding(n_items, layers[-1], sparse=True)
      self.By = nn.Embedding(n_items, 1, sparse=True)
      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 GRU4RecModel object from inherited base classes: nn with example: obj = GRU4RecModel(n_items, P0, layers, dropout_p_embed, dropout_p_hidden, embedding, constrained_embedding, logq, sample_alpha, bpreg, elu_param, loss)