code: def test_reduce_dataset_reduces_size_and_precision(
      X,
      x_type,
      dtype,
      y,
      is_classification,
      memory_allocation,
      operations,
  ):
      assert len(X) == len(y)
      X = X.astype(dtype)
      if x_type == csr_matrix:
          X = x_type(X)
  
      random_state = 0
      memory_limit = 2  # Force reductions
  
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore")
  
          X_out, y_out = reduce_dataset_size_if_too_large(
              X=X,
              y=y,
              random_state=random_state,
              memory_limit=memory_limit,
              operations=operations,
              memory_allocation=memory_allocation,
              is_classification=is_classification,
          )
  
      def bytes(arr):
          return (
              arr.nbytes
              if isinstance(arr, np.ndarray)
              else arr.data.nbytes
          )
  
      # If we expect some precision reduction unless at float32 already
      if "precision" in operations and dtype != np.float32:
          expected = reduction_mapping[X.dtype]
          assert X_out.dtype == expected
          assert bytes(X_out) < bytes(X)
  
      # If we expect some subsampling
      if "subsample" in operations:
          assert X_out.shape[0] < X.shape[0]
          assert y_out.shape[0] < y.shape[0]
          assert bytes(X_out) < bytes(X)
  
imports: from typing import Any, Dict, List, Union
  import warnings
  from itertools import chain
  import numpy
  import pandas
  import sklearn.datasets
  from scipy.sparse import csr_matrix, spmatrix
  from autosklearn.constants import BINARY_CLASSIFICATION, CLASSIFICATION_TASKS, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, REGRESSION, REGRESSION_TASKS
  from autosklearn.util.data import default_dataset_compression_arg, reduce_dataset_size_if_too_large, reduce_precision, reduction_mapping, subsample, supported_precision_reductions, validate_dataset_compression_arg
  import pytest
label_desc: Use image node_id 15 for calling a global function with example usage: test_reduce_dataset_reduces_size_and_precision(X, x_type, dtype, y, is_classification, memory_allocation, operations) and returns: unknown