code: def _get_label_from_constructed_dataset(
      dataset: Dataset,
  ) -> np.ndarray:
      label = dataset.get_label()
      error_msg = (
          "Estimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. "
          "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues."
      )
      assert isinstance(label, np.ndarray), error_msg
      return label
  
imports: import copy
  from inspect import signature
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  from .basic import Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _LGBM_BoosterBestScoreType, _LGBM_CategoricalFeatureConfiguration, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration, _LGBM_GroupType, _LGBM_InitScoreType, _LGBM_LabelType, _LGBM_WeightType, _log_warning
  from .callback import _EvalResultDict, record_evaluation
  from .compat import SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray, _LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase, _LGBMComputeSampleWeight, _LGBMCpuCount, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, dt_DataTable, np_random_Generator, pd_DataFrame
  from .engine import train
label_desc: Use image node_id 2 for calling a global function with example usage: _get_label_from_constructed_dataset(dataset) and returns: label