code: def train_step(self, data):
      X, y = data
      mask = self.g.ndata["train_mask"]
  
      with tf.GradientTape() as tape:
          y_pred = self(X, training=True)
          loss = self.compiled_loss(y[mask], y_pred[mask])
  
      trainable_variables = self.trainable_variables
      gradients = tape.gradient(loss, trainable_variables)
      self.optimizer.apply_gradients(
          zip(gradients, trainable_variables)
      )
      self.compiled_metrics.update_state(y[mask], y_pred[mask])
      return {m.name: m.result() for m in self.metrics}
  
imports: import argparse
  import textwrap
  import tensorflow
  import tensorflow_addons
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
  from dgl.nn.tensorflow.conv import SGConv
label_desc: Use image node_id 6 for calling the SGC obj's underlying member method code with example usage: obj.train_step(data) and returns: unknown