code: def post_process(self, logits, label_names):
      logits_list = []
      # tgt_list = []
  
      for idx in range(len(logits)):
          logits_list.append(logits[idx].cpu().clone().numpy())
      pred_tags = [label_names[int(pred)] for pred in logits_list]
      return pred_tags
  
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 4 for calling the Word2tag obj's underlying member method code with example usage: obj.post_process(logits, label_names) and returns: pred_tags