From 9b62e8d01492e0315cded0d77b9abace1b71527d Mon Sep 17 00:00:00 2001
From: "Hongzhi (Steve), Chen" <chenhongzhi.nkcs@gmail.com>
Date: Fri, 21 Oct 2022 13:32:52 +0800
Subject: [PATCH] Revert "[Misc] Isort
 tutorials/large/L1_large_node_classification.py (#4721)" (#4741)

This reverts commit 9962b7bdfdd327d421725e2554e08dbf4fd68afb.
---
 tutorials/large/L1_large_node_classification.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/tutorials/large/L1_large_node_classification.py b/tutorials/large/L1_large_node_classification.py
index bc6f3a508836..4ff1aa5b76d4 100644
--- a/tutorials/large/L1_large_node_classification.py
+++ b/tutorials/large/L1_large_node_classification.py
@@ -25,6 +25,9 @@
 # OGB already prepared the data as DGL graph.
 #
 
+import dgl
+import torch
+import numpy as np
 from ogb.nodeproppred import DglNodePropPredDataset
 
 dataset = DglNodePropPredDataset("ogbn-arxiv")
@@ -37,8 +40,6 @@
 # simply get the graph and its node labels like this:
 #
 
-import dgl
-
 graph, node_labels = dataset[0]
 # Add reverse edges since ogbn-arxiv is unidirectional.
 graph = dgl.add_reverse_edges(graph)
@@ -165,8 +166,6 @@
 # the computation of the new features.
 #
 
-import torch
-
 mfg_0_src = mfgs[0].srcdata[dgl.NID]
 mfg_0_dst = mfgs[0].dstdata[dgl.NID]
 print(mfg_0_src)
@@ -184,7 +183,6 @@
 
 import torch.nn as nn
 import torch.nn.functional as F
-
 from dgl.nn import SAGEConv
 
 
@@ -289,9 +287,8 @@ def forward(self, mfgs, x):
 # It also saves the model with the best validation accuracy into a file.
 #
 
-import numpy as np
-import sklearn.metrics
 import tqdm
+import sklearn.metrics
 
 best_accuracy = 0
 best_model_path = "model.pt"