[Misc] Isort tutorials/large/L1_large_node_classification.py (#4721)

* iosrt

* sort
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

tutorials/large/L1_large_node_classification.py

@@ -25,9 +25,6 @@ Sampling for GNN Training <L0_neighbor_sampling_overview>`.
 # 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")
@@ -40,6 +37,8 @@ device = "cpu"  # change to 'cuda' for GPU
 # 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)
@@ -166,6 +165,8 @@ print(
 # 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)
@@ -183,6 +184,7 @@ print(torch.equal(mfg_0_src[: mfgs[0].num_dst_nodes()], mfg_0_dst))
 
 import torch.nn as nn
 import torch.nn.functional as F
+
 from dgl.nn import SAGEConv
 
 
@@ -287,8 +289,9 @@ valid_dataloader = dgl.dataloading.DataLoader(
 # It also saves the model with the best validation accuracy into a file.
 #
 
-import tqdm
+import numpy as np
 import sklearn.metrics
+import tqdm
 
 best_accuracy = 0
 best_model_path = "model.pt"