code: def dataset_with_single_grouping(request):
      n, k, np_type = request.param
      np.random.seed(42)
      X = np.random.normal(0, 2, (n, k)).astype(np_type)
      y = np.random.normal(0, 2, (n,))
  
      X, y = check_X_y(X, y)
  
      # Make sure all groups are present
      groups = np.repeat([0, 1], repeats=(X.shape[0] + 1) // 2)[
          : X.shape[0], np.newaxis
      ]
      X_with_groups = np.concatenate([groups, X], axis=1)
      grouper = 0  # First column
  
      return X, y, groups, X_with_groups, grouper
  
imports: import itertools
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  from sklearn.utils import check_X_y
  from sklego.common import flatten
  from sklego.datasets import load_penguins
  from sklego.meta import GroupedTransformer
  from tests.conftest import general_checks, k_vals, n_vals, np_types, select_tests, transformer_checks
label_desc: Use image node_id 2 for calling a global function with example usage: dataset_with_single_grouping(request) and returns: X,  y,  groups,  X_with_groups,  grouper