code: def test_fetch_lawschool_gpa():
      """Tests Law School GPA dataset shapes with various options."""
      gpa = fetch_lawschool_gpa()
      assert len(gpa) == 2
      assert gpa.X.shape == (22342, 3)
      assert gpa.y.nunique() > 2  # regression
      assert fetch_lawschool_gpa(
          numeric_only=True, dropna=False
      ).X.shape == (22342, 3)
  
imports: from functools import partial
  import numpy
  from numpy.testing import assert_array_equal
  import pandas
  from pandas.api.types import is_numeric_dtype
  from pandas.testing import assert_frame_equal
  import pytest
  from sklearn.compose import make_column_transformer
  from sklearn.preprocessing import OneHotEncoder, minmax_scale
  from aif360.datasets import AdultDataset, GermanDataset, CompasDataset, LawSchoolGPADataset, MEPSDataset19, MEPSDataset20, MEPSDataset21
  from aif360.sklearn.datasets import standardize_dataset, NumericConversionWarning, fetch_adult, fetch_bank, fetch_german, fetch_compas, fetch_lawschool_gpa, fetch_meps
label_desc: Use image node_id 17 for calling a global function with example usage: test_fetch_lawschool_gpa() without return types