code: def reduce_precision(
      X: Union[np.ndarray, spmatrix]
  ) -> Tuple[Union[np.ndarray, spmatrix], Type]:
      """Reduces the precision of a np.ndarray or spmatrix containing floats
  
      Parameters
      ----------
      X:  np.ndarray | spmatrix
          The data to reduce precision of.
  
      Returns
      -------
      (ndarray | spmatrix, dtype)
          Returns the reduced data X along with the dtype it was reduced to.
      """
      if X.dtype not in supported_precision_reductions:
          raise ValueError(
              f"X.dtype = {X.dtype} not equal to any supported"
              f" {supported_precision_reductions}"
          )
  
      precision = reduction_mapping[X.dtype]
      return X.astype(precision), precision
  
imports: from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple, Type, Union, cast
  import warnings
  import numpy
  import pandas
  from scipy.sparse import spmatrix
  from sklearn.model_selection import train_test_split
  from autosklearn.evaluation.splitter import CustomStratifiedShuffleSplit
label_desc: Use image node_id 8 for calling a global function with example usage: reduce_precision(X) and returns: X,  precision