code: def plot_points(data, colors):
      model.eval()
      z = model.encode(data.x, data.edge_index)
      z = TSNE(n_components=2).fit_transform(z.cpu().numpy())
      y = data.y.cpu().numpy()
  
      plt.figure(figsize=(8, 8))
      for i in range(dataset.num_classes):
          plt.scatter(z[y == i, 0], z[y == i, 1], s=20, color=colors[i])
      plt.axis("off")
      plt.show()
  
imports: import os.path
  import matplotlib.pyplot
  import torch
  from sklearn.cluster import KMeans
  from sklearn.manifold import TSNE
  from sklearn.metrics.cluster import completeness_score, homogeneity_score, v_measure_score
  from torch.nn import Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import ARGVA, GCNConv
label_desc: Use image node_id 3 for calling a global function with example usage: plot_points(data, colors) without return types