code: def __init__(self, channels, n_layers):
      super().__init__()
      self.conv1 = GINConv(
          channels, epsilon=0, mlp_hidden=[channels, channels]
      )
      self.convs = []
      for _ in range(1, n_layers):
          self.convs.append(
              GINConv(
                  channels, epsilon=0, mlp_hidden=[channels, channels]
              )
          )
      self.pool = GlobalAvgPool()
      self.dense1 = Dense(channels, activation="relu")
      self.dropout = Dropout(0.5)
      self.dense2 = Dense(n_out, activation="softmax")
  
file_path: spektral/examples/graph_prediction/tud_gin.py
imports: import numpy
  import tensorflow
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.losses import CategoricalCrossentropy
  from tensorflow.keras.metrics import categorical_accuracy
  from tensorflow.keras.models import Model
  from tensorflow.keras.optimizers import Adam
  from spektral.data import DisjointLoader
  from spektral.datasets import TUDataset
  from spektral.layers import GINConv, GlobalAvgPool
label_desc: Use image node_id 1 to create a new GIN0 object from inherited base classes: Model with example: obj = GIN0(channels, n_layers)