code: def inference_forward(self, collate_data):
      batch_graph = collate_data["graph_data"].to(self.device)
      return self.forward(batch_graph, tgt=None, require_loss=False)[0]
  
imports: import os
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  from graph4nlp.pytorch.modules.graph_construction import NodeEmbeddingBasedRefinedGraphConstruction
  from graph4nlp.pytorch.modules.graph_construction.node_embedding_based_graph_construction import NodeEmbeddingBasedGraphConstruction
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.graph_embedding_initialization.graph_embedding_initialization import GraphEmbeddingInitialization
  from graph4nlp.pytorch.modules.graph_embedding_learning.gat import GAT
  from graph4nlp.pytorch.modules.graph_embedding_learning.gcn import GCN
  from graph4nlp.pytorch.modules.graph_embedding_learning.ggnn import GGNN
  from graph4nlp.pytorch.modules.graph_embedding_learning.graphsage import GraphSAGE
  from graph4nlp.pytorch.modules.prediction.classification.node_classification import BiLSTMFeedForwardNN
label_desc: Use image node_id 3 for calling the Word2tag obj's underlying member method code with example usage: obj.inference_forward(collate_data) and returns: self