Specify src path for isort. (#8867)

demo/dask/cpu_survival.py

@@ -8,9 +8,9 @@ import os
 
 import dask.dataframe as dd
 from dask.distributed import Client, LocalCluster
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
+from xgboost.dask import DaskDMatrix
 
 
 def main(client):

demo/dask/cpu_training.py

@@ -5,9 +5,9 @@ Example of training with Dask on CPU
 """
 from dask import array as da
 from dask.distributed import Client, LocalCluster
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
+from xgboost.dask import DaskDMatrix
 
 
 def main(client):

demo/dask/dask_callbacks.py

@@ -6,9 +6,9 @@ import numpy as np
 from dask.distributed import Client, LocalCluster
 from dask_ml.datasets import make_regression
 from dask_ml.model_selection import train_test_split
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
+from xgboost.dask import DaskDMatrix
 
 
 def probability_for_going_backward(epoch):

demo/dask/gpu_training.py

@@ -7,10 +7,10 @@ from dask import array as da
 from dask import dataframe as dd
 from dask.distributed import Client
 from dask_cuda import LocalCUDACluster
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
 from xgboost import dask as dxgb
+from xgboost.dask import DaskDMatrix
 
 
 def using_dask_matrix(client: Client, X, y):

demo/guide-python/spark_estimator_examples.py

@@ -10,6 +10,7 @@ from pyspark.ml.linalg import Vectors
 from pyspark.sql import SparkSession
 from pyspark.sql.functions import rand
 from sklearn.model_selection import train_test_split
+
 from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor
 
 spark = SparkSession.builder.master("local[*]").getOrCreate()

demo/nvflare/custom/controller.py

@@ -4,7 +4,6 @@ Example of training controller with NVFlare
 """
 import multiprocessing
 
-import xgboost.federated
 from nvflare.apis.client import Client
 from nvflare.apis.fl_context import FLContext
 from nvflare.apis.impl.controller import Controller, Task
@@ -12,6 +11,8 @@ from nvflare.apis.shareable import Shareable
 from nvflare.apis.signal import Signal
 from trainer import SupportedTasks
 
+import xgboost.federated
+
 
 class XGBoostController(Controller):
     def __init__(self, port: int, world_size: int, server_key_path: str,