code: def __init__(self, in_channels, hidden_channels, out_channels):
      super().__init__()
      self.conv1 = GCNConv(in_channels, hidden_channels)
      self.conv_mu = GCNConv(hidden_channels, out_channels)
      self.conv_logstd = GCNConv(hidden_channels, out_channels)
  
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 1 to create a new Encoder object from inherited base classes: torch.nn with example: obj = Encoder(in_channels, hidden_channels, out_channels)