code: def __init__(
      self,
      encoder_output_dim,
      hidden_dim,
      output_dim,
      topk_labels_per_source_token=None,
      use_self_attention=False,
  ):
      super().__init__()
      self.encoder_output_dim = encoder_output_dim
      self.hidden_dim = hidden_dim
      self.output_dim = output_dim
      self.topk_labels_per_source_token = topk_labels_per_source_token
      self.use_self_attention = use_self_attention
  
      if self.use_self_attention:
          self.init_layer = nn.Linear(
              encoder_output_dim, encoder_output_dim
          )
          self.attn_layer = nn.Linear(2 * encoder_output_dim, 1)
          self.hidden_layer = nn.Linear(
              2 * encoder_output_dim, hidden_dim
          )
          self.output_layer = nn.Linear(hidden_dim, output_dim)
      else:
          self.hidden_layer = nn.Linear(encoder_output_dim, hidden_dim)
          self.output_layer = nn.Linear(hidden_dim, output_dim)
  
file_path: translate/pytorch_translate/word_prediction/word_predictor.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new WordPredictor object from inherited base classes: nn with example: obj = WordPredictor(encoder_output_dim, hidden_dim, output_dim, topk_labels_per_source_token, use__attention)