code: def __init__(self, words, args):
      super(Model, self).__init__()
      self.args = args
      if args.n_e:
          self.n_e = args.n_e
      else:
          self.n_e = len(words) if len(words) < args.n_d else args.n_d
      self.n_d = args.n_d
      self.depth = args.depth
      self.drop = nn.Dropout(args.dropout)
      self.embedding_layer = nn.Embedding(len(words), self.n_e)
      self.n_V = len(words)
      if args.lstm:
          self.rnn = nn.LSTM(
              self.n_e, self.n_d, self.depth, dropout=args.dropout
          )
      else:
          self.rnn = sru.SRU(
              self.n_e,
              self.n_d,
              self.depth,
              dropout=args.dropout,
              n_proj=args.n_proj,
              # use_tanh = 0,
              highway_bias=args.bias,
              layer_norm=args.layer_norm,
          )
      self.output_layer = nn.Linear(self.n_d, self.n_V)
      self.init_weights()
  
imports: import sys
  import os
  import argparse
  import time
  import random
  import math
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torch.nn.functional
  from torch.autograd import Variable
  from tensorboardX import SummaryWriter
  import sru
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(words, args)