code: def test_chunked_dataset_linear():
      X_train, X_test, y_train, y_test = train_test_split(
          *load_breast_cancer(return_X_y=True),
          test_size=0.1,
          random_state=2,
      )
      chunk_size = X_train.shape[0] // 10 + 1
      X_train = [
          X_train[i * chunk_size : (i + 1) * chunk_size, :]
          for i in range(X_train.shape[0] // chunk_size + 1)
      ]
      X_test = [
          X_test[i * chunk_size : (i + 1) * chunk_size, :]
          for i in range(X_test.shape[0] // chunk_size + 1)
      ]
      params = {"bin_construct_sample_cnt": 100, "linear_tree": True}
      train_data = lgb.Dataset(X_train, label=y_train, params=params)
      valid_data = train_data.create_valid(
          X_test, label=y_test, params=params
      )
      train_data.construct()
      valid_data.construct()
  
imports: import filecmp
  import numbers
  import re
  from copy import deepcopy
  from os import getenv
  from pathlib import Path
  import numpy
  import pytest
  from scipy import sparse
  from sklearn.datasets import dump_svmlight_file, load_svmlight_file
  from sklearn.model_selection import train_test_split
  import lightgbm
  from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame, pd_Series
  from .utils import dummy_obj, load_breast_cancer, mse_obj, np_assert_array_equal
label_desc: Use image node_id 6 for calling a global function with example usage: test_chunked_dataset_linear() without return types