code: def test_reduce_dataset_subsampling_explicit_values(
      memory_limit, precision, task
  ):
      random_state = 0
      fixture = {
          BINARY_CLASSIFICATION: {
              1: {
                  float: 2621,
                  np.float32: 2621,
                  np.float64: 2621,
                  np.float128: 1310,
              },
              100: {
                  float: 12000,
                  np.float32: 12000,
                  np.float64: 12000,
                  np.float128: 12000,
              },
          },
          MULTICLASS_CLASSIFICATION: {
              1: {
                  float: 409,
                  np.float32: 409,
                  np.float64: 409,
                  np.float128: 204,
              },
              100: {
                  float: 1797,
                  np.float32: 1797,
                  np.float64: 1797,
                  np.float128: 1797,
              },
          },
          MULTILABEL_CLASSIFICATION: {
              1: {
                  float: 409,
                  np.float32: 409,
                  np.float64: 409,
                  np.float128: 204,
              },
              100: {
                  float: 1797,
                  np.float32: 1797,
                  np.float64: 1797,
                  np.float128: 1797,
              },
          },
          REGRESSION: {
              1: {
                  float: 1310,
                  np.float32: 1310,
                  np.float64: 1310,
                  np.float128: 655,
              },
              100: {
                  float: 5000,
                  np.float32: 5000,
                  np.float64: 5000,
                  np.float128: 5000,
              },
          },
          MULTIOUTPUT_REGRESSION: {
              1: {
                  float: 1310,
                  np.float32: 1310,
                  np.float64: 1310,
                  np.float128: 655,
              },
              100: {
                  float: 5000,
                  np.float32: 5000,
                  np.float64: 5000,
                  np.float128: 5000,
              },
          },
      }
  
      # Create the task and data
      if task == BINARY_CLASSIFICATION:
          X, y = sklearn.datasets.make_hastie_10_2()
      elif task == MULTICLASS_CLASSIFICATION:
          X, y = sklearn.datasets.load_digits(return_X_y=True)
      elif task == MULTILABEL_CLASSIFICATION:
          X, y_ = sklearn.datasets.load_digits(return_X_y=True)
          y = np.zeros((X.shape[0], 10))
          for i, j in enumerate(y_):
              y[i, j] = 1
      elif task == REGRESSION:
          X, y = sklearn.datasets.make_friedman1(
              n_samples=5000, n_features=20
          )
      elif task == MULTIOUTPUT_REGRESSION:
          X, y = sklearn.datasets.make_friedman1(
              n_samples=5000, n_features=20
          )
          y = np.vstack((y, y)).transpose()
      else:
          raise ValueError(task)
  
      # Validate the test data and make sure X and y have the same number of rows
      assert X.shape[0] == y.shape[0]
  
      # Convert X to the dtype we are testing
      X = X.astype(precision)
  
      # Preform the subsampling through `reduce_dataset_size_if_too_large`
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          X_new, y_new = reduce_dataset_size_if_too_large(
              X=X,
              y=y,
              random_state=random_state,
              memory_limit=memory_limit,
              is_classification=task in CLASSIFICATION_TASKS,
              operations=["precision", "subsample"],
              memory_allocation=0.1,
          )
  
      # Assert the new number of samples
      assert X_new.shape[0] == fixture[task][memory_limit][precision]
  
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 21 for calling a global function with example usage: test_reduce_dataset_subsampling_explicit_values(memory_limit, precision, task) without return types