code: def test_sequence_get_data(num_seq):
      nrow = 20
      ncol = 11
      data = np.arange(nrow * ncol, dtype=np.float64).reshape(
          (nrow, ncol)
      )
      X = data[:, :-1]
      Y = data[:, -1]
  
      seqs = _create_sequence_from_ndarray(
          data=X, num_seq=num_seq, batch_size=6
      )
      seq_ds = lgb.Dataset(
          seqs, label=Y, params=None, free_raw_data=False
      ).construct()
      assert seq_ds.get_data() == seqs
  
      used_indices = np.random.choice(
          np.arange(nrow), nrow // 3, replace=False
      )
      subset_data = seq_ds.subset(used_indices).construct()
      np.testing.assert_array_equal(
          subset_data.get_data(), X[sorted(used_indices)]
      )
  
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 4 for calling a global function with example usage: test_sequence_get_data(num_seq) without return types