code: def test_subsample_validity(
      X, x_type, y, y_type, random_state, sample_size, task
  ):
      """Asserts the validity of the function with all valid types
  
      We want to make sure that `subsample` works correctly with all the types listed
      as x_type and y_type.
  
      We also want to make sure it works with all kinds of target types.
  
      The output should maintain the types, and subsample the correct amount.
      """
      assert len(X) == len(y)  # Make sure our test data is correct
  
      if y_type == pd.Series and task in [
          MULTILABEL_CLASSIFICATION,
          MULTIOUTPUT_REGRESSION,
      ]:
          # We can't have a pd.Series with multiple values as it's 1 dimensional
          pytest.skip(
              "Can't have pd.Series as y when task is n-dimensional"
          )
  
      # Convert our data to its given x_type or y_type
      def convert(arr, objtype):
          if objtype == np.ndarray:
              return arr
          elif objtype == list:
              return arr.tolist()
          else:
              return objtype(arr)
  
      X = convert(X, x_type)
      y = convert(y, y_type)
  
      # Subsample the data, ignoring any warnings
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          X_sampled, y_sampled = subsample(
              X,
              y,
              random_state=random_state,
              sample_size=sample_size,
              is_classification=task in CLASSIFICATION_TASKS,
          )
  
      # Function to get the type of an obj
      def dtype(obj):
          if isinstance(obj, List):
              if isinstance(obj[0], List):
                  return type(obj[0][0])
              else:
                  return type(obj[0])
  
          elif isinstance(obj, pd.DataFrame):
              return obj.dtypes
  
          else:
              return obj.dtype
  
      # Check that the types of X remain the same after subsampling
      if isinstance(X, pd.DataFrame):
          # Dataframe can have multiple types, one per column
          assert list(dtype(X_sampled)) == list(dtype(X))
      else:
          assert dtype(X_sampled) == dtype(X)
  
      # Check that the types of y remain the same after subsampling
      if isinstance(y, pd.DataFrame):
          assert list(dtype(y_sampled)) == list(dtype(y))
      else:
          assert dtype(y_sampled) == dtype(y)
  
      # Function to get the size of an object
      def size(obj):
          if isinstance(
              obj, spmatrix
          ):  # spmatrix doesn't support __len__
              return obj.shape[0] if obj.shape[0] > 1 else obj.shape[1]
          else:
              return len(obj)
  
      # check the right amount of samples were taken
      if sample_size < 1:
          assert size(X_sampled) == int(sample_size * size(X))
      else:
          assert size(X_sampled) == sample_size
  
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 12 for calling a global function with example usage: test_subsample_validity(X, x_type, y, y_type, random_state, sample_size, task) and returns: arr, unknown, len, arr, objtype, type, type, obj, obj