code: def __init__(
      self,
      vocab_size,
      embedding_dim,
      H1,
      H2,
      H3,
      D_pair_in,
      D_single_in,
      dropout=0.5,
  ):
      super(Model, self).__init__()
      self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
      self.drop = nn.Dropout(dropout)
      self.pair_top = nn.Sequential(
          nn.Linear(D_pair_in, H1),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(H1, H2),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(H2, H3),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(H3, 1),
          nn.Linear(1, 1),
      )
      self.single_top = nn.Sequential(
          nn.Linear(D_single_in, H1),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(H1, H2),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(H2, H3),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(H3, 1),
          nn.Linear(1, 1),
      )
      self.init_weights()
  
file_path: neuralcoref/neuralcoref/train/model.py
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.utils.data
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(vocab_size, embedding_dim, H1, H2, H3, D_pair_in, D_single_in, dropout)