[example] update docstring (#6902)

examples/sampling/graphbolt/rgcn/hetero_rgcn.py

@@ -136,7 +136,11 @@ def create_dataloader(
     # Move the mini-batch to the appropriate device.
     # `device`:
     #   The device to move the mini-batch to.
-    # [TODO] Moving `MiniBatch` to GPU is not supported yet.
+    # [Rui] Usually, we move the mini-batch to target device in the datapipe.
+    # However, in this example, we leaves the mini-batch on CPU and move it to
+    # GPU after blocks are created. This is because this example is busy on
+    # GPU due to embedding layer. And block creation on CPU could be overlapped
+    # with optimization operation on GPU and it results in better performance.
     device = torch.device("cpu")
     datapipe = datapipe.copy_to(device)
 
@@ -443,6 +447,7 @@ def evaluate(
     y_true = list()
 
     for data in tqdm(data_loader, desc="Inference"):
+        # Convert MiniBatch to DGL Blocks and move them to the target device.
         blocks = [block.to(device) for block in data.blocks]
         node_features = extract_node_features(
             name, blocks[0], data, node_embed, device
@@ -508,7 +513,8 @@ def train(
         total_loss = 0
 
         for data in tqdm(data_loader, desc=f"Training~Epoch {epoch + 1:02d}"):
-            # Convert MiniBatch to DGL Blocks.
+            # Convert MiniBatch to DGL Blocks and move them to the target
+            # device.
             blocks = [block.to(device) for block in data.blocks]
 
             # Fetch the number of seed nodes in the batch.