code: def __init__(self, args, dictionary, embed_tokens):
      super().__init__(args, dictionary, embed_tokens)
      self.emb_ctx = nn.GRU(
          input_size=embed_tokens.embedding_dim,
          hidden_size=embed_tokens.embedding_dim // 2,
          num_layers=1,
          bidirectional=True,
      )
  
file_path: fairseq/examples/byte_level_bpe/gru_transformer.py
imports: import torch.nn
  import torch.nn.functional
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.transformer import TransformerEncoder, TransformerModel
label_desc: Use image node_id 1 to create a new GRUTransformerEncoder object from inherited base classes: TransformerEncoder with example: obj = GRUTransformerEncoder(args, dictionary, embed_tokens)