code: def test_generate_train_test_split_edgecase():
      """
      Test rare edge-cases when data has many classes or very few samples when doing train test splits.
      """
      data = pd.DataFrame(index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
      data["label"] = [0, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5]
  
      for test_size in range(1, 12):
          """
          Normal Case: Regression should always work
          """
          X_train, X_test, y_train, y_test = generate_train_test_split(
              X=data,
              y=data["label"],
              problem_type="regression",
              test_size=test_size,
          )
          assert len(X_train) == len(y_train)
          assert list(X_train.index) == list(y_train.index)
          assert len(X_test) == len(y_test)
          assert list(X_test.index) == list(y_test.index)
          assert len(X_test) == test_size
          assert len(X_train) == len(data) - test_size
  
      for test_size in range(1, 6):
          """
          Edge-case: There are fewer test rows than classes
           This only works because of special try/except logic in `generate_train_test_split`.
          """
          X_train, X_test, y_train, y_test = generate_train_test_split(
              X=data,
              y=data["label"],
              problem_type="multiclass",
              test_size=test_size,
          )
          assert len(X_train) == len(y_train)
          assert list(X_train.index) == list(y_train.index)
          assert len(X_test) == len(y_test)
          assert list(X_test.index) == list(y_test.index)
          assert len(X_test) <= test_size
          assert len(X_train) == len(data) - len(X_test)
  
      for test_size in range(6, 7):
          """
          Normal Case
          """
          X_train, X_test, y_train, y_test = generate_train_test_split(
              X=data,
              y=data["label"],
              problem_type="multiclass",
              test_size=test_size,
          )
          assert len(X_train) == len(y_train)
          assert list(X_train.index) == list(y_train.index)
          assert len(X_test) == len(y_test)
          assert list(X_test.index) == list(y_test.index)
          assert len(X_test) <= test_size
          assert len(X_train) == len(data) - len(X_test)
  
      for test_size in range(7, 12):
          """
          Edge-case: There are fewer train rows than classes
          Error due to not enough training data to have at least one instance of every class in train.
          Note: Ideally this shouldn't raise an exception, but writing the logic to avoid the error is tricky and the scenario should never occur in practice.
          """
          with pytest.raises(ValueError):
              (
                  X_train,
                  X_test,
                  y_train,
                  y_test,
              ) = generate_train_test_split(
                  X=data,
                  y=data["label"],
                  problem_type="multiclass",
                  test_size=test_size,
              )
  
imports: import unittest
  import numpy
  import pandas
  import pytest
  from autogluon.core.constants import BINARY, MULTICLASS, MULTICLASS_UPPER_LIMIT, REGRESSION
  from autogluon.core.utils import infer_problem_type
  from autogluon.core.utils.utils import generate_train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: test_generate_train_test_split_edgecase() without return types