code: def plot_embeddings(
      embeddings,
  ):
      X, Y = read_node_label(
          "../data/flight/labels-brazil-airports.txt", skip_head=True
      )
  
      emb_list = []
  
      for k in X:
          emb_list.append(embeddings[k])
  
      emb_list = np.array(emb_list)
  
      model = TSNE(n_components=2)
  
      node_pos = model.fit_transform(emb_list)
  
      color_idx = {}
  
      for i in range(len(X)):
          color_idx.setdefault(Y[i][0], [])
  
          color_idx[Y[i][0]].append(i)
  
      for c, idx in color_idx.items():
          plt.scatter(
              node_pos[idx, 0], node_pos[idx, 1], label=c
          )  # c=node_colors)
  
      plt.legend()
  
      plt.show()
  
file_path: GraphEmbedding/examples/node2vec_flight.py
imports: import numpy
  from ge.classify import read_node_label, Classifier
  from ge import Node2Vec
  from sklearn.linear_model import LogisticRegression
  import matplotlib.pyplot
  import networkx
  from sklearn.manifold import TSNE
label_desc: Use image node_id 2 for calling a global function with example usage: plot_embeddings(embeddings) without return types