code: def __init__(
      self,
      word_vocab,
      embedding_style,
      hidden_size=None,
      fix_word_emb=True,
      fix_bert_emb=True,
      word_dropout=None,
      rnn_dropout=None,
  ):
      super(GraphEmbeddingInitialization, self).__init__()
      self.embedding_layer = EmbeddingConstruction(
          word_vocab,
          embedding_style["single_token_item"],
          emb_strategy=embedding_style["emb_strategy"],
          hidden_size=hidden_size,
          num_rnn_layers=embedding_style.get("num_rnn_layers", 1),
          fix_word_emb=fix_word_emb,
          fix_bert_emb=fix_bert_emb,
          bert_model_name=embedding_style.get(
              "bert_model_name", "bert-base-uncased"
          ),
          bert_lower_case=embedding_style.get("bert_lower_case", True),
          word_dropout=word_dropout,
          rnn_dropout=rnn_dropout,
      )
  
imports: from abc import abstractmethod
  import torch.nn
  from graph4nlp.pytorch.data.data import GraphData
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import EmbeddingConstruction
label_desc: Use image node_id 1 to create a new GraphEmbeddingInitialization object from inherited base classes: nn with example: obj = GraphEmbeddingInitialization(word_vocab, embedding_style, hidden_size, fix_word_emb, fix_bert_emb, word_dropout, rnn_dropout)