code: def test_fit_and_predict_with_1d_targets_as_2d(
      regressor: Type[RegressorChoice], X: np.ndarray, y: np.ndarray
  ) -> None:
      """Test that all pipelines work with 1d target types when they are wrapped as 2d
  
      Parameters
      ----------
      regressor: RegressorChoice
          The regressor to test
  
      X: np.ndarray
          The features
  
      y: np.ndarray
          The 1d targets wrapped as 2d
  
      Expects
      -------
      * Should be able to fit with 1d targets wrapped in 2d
      * Should be able to predict 1d targets wrapped in 2d
      * Should return 1d predictions
      * Should have predictions with the same length as the y
      """
      assert len(X) == len(y)
      assert y.ndim == 2 and y.shape[1] == 1
  
      config_space = regressor.get_hyperparameter_search_space()
      default_config = config_space.get_default_configuration()
  
      model = regressor(random_state=0, **default_config)
  
      with ignore_warnings(regressor_warnings):
          model.fit(X, y)
  
      predictions = model.predict(X)
  
      assert predictions.ndim == 1
      assert len(predictions) == len(y)
  
imports: from typing import Container, Type
  import numpy
  import sklearn.metrics
  from autosklearn.pipeline.components.regression import RegressorChoice, _regressors
  from autosklearn.pipeline.components.regression.libsvm_svr import LibSVM_SVR
  from autosklearn.pipeline.constants import SPARSE
  from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit
  import pytest
  import unittest
  from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings
label_desc: Use image node_id 2 for calling a global function with example usage: test_fit_and_predict_with_1d_targets_as_2d(regressor, X, y) without return types