code: def medium_size_counts():
      """X, y pair for classification with N >> p.
  
      The samples outnumber the total features, leading to
      greater stability of the solutions. Useful for testing
      the accuracy of solvers.
      """
      sample_size = 500
      n_features = 100
      X, y = make_counts(
          chunks=100,
          n_samples=sample_size,
          n_features=n_features,
          n_informative=n_features,
          random_state=0,
          scale=1 / np.sqrt(n_features),
      )
      return X, y
  
imports: import contextlib
  import string
  import dask.dataframe
  import numpy
  import pytest
  from distributed.utils_test import cluster
  from dask_ml.datasets import make_blobs, make_classification, make_counts, make_regression
label_desc: Use image node_id 16 for calling a global function with example usage: medium_size_counts() and returns: X,  y