code: def test_sample_y_shape_with_prior(n_targets, n_samples):
      """Check the output shape of `sample_y` is consistent before and after `fit`."""
      rng = np.random.RandomState(1024)
  
      X = rng.randn(10, 3)
      y = rng.randn(10, n_targets if n_targets is not None else 1)
  
      model = GaussianProcessRegressor(n_targets=n_targets)
      shape_before_fit = model.sample_y(X, n_samples=n_samples).shape
      model.fit(X, y)
      shape_after_fit = model.sample_y(X, n_samples=n_samples).shape
      assert shape_before_fit == shape_after_fit
  
imports: import re
  import sys
  import warnings
  import numpy
  import pytest
  from scipy.optimize import approx_fprime
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import RBF, DotProduct, ExpSineSquared, WhiteKernel
  from sklearn.gaussian_process.kernels import ConstantKernel
  from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_less
label_desc: Use image node_id 32 for calling a global function with example usage: test_sample_y_shape_with_prior(n_targets, n_samples) without return types