code: def test_draw_kernel_feature_paths(self):
      for seed, models in enumerate(self.models.values()):
          for model, sample_shape in product(
              models, [Size([1024]), Size([2, 512])]
          ):
              with torch.random.fork_rng():
                  torch.random.manual_seed(seed)
                  paths = draw_kernel_feature_paths(
                      model=model,
                      sample_shape=sample_shape,
                      num_features=self.num_features,
                  )
                  self.assertIsInstance(paths, GeneralizedLinearPath)
                  self._test_draw_kernel_feature_paths(
                      model, paths, sample_shape
                  )
  
      with self.subTest("test_model_list"):
          model_list = ModelListGP(
              self.models["inferred"][0], self.models["observed"][0]
          )
          path_list = draw_kernel_feature_paths(
              model=model_list,
              sample_shape=sample_shape,
              num_features=self.num_features,
          )
          (train_X,) = get_train_inputs(
              model_list.models[0], transformed=False
          )
          X = torch.zeros(
              4,
              train_X.shape[-1],
              dtype=train_X.dtype,
              device=self.device,
          )
          sample_list = path_list(X)
          self.assertIsInstance(path_list, PathList)
          self.assertIsInstance(sample_list, list)
          self.assertEqual(len(sample_list), len(path_list.paths))
  
      with self.subTest("test_initialization"):
          model = self.models["inferred"][0]
          sample_shape = torch.Size([16])
          expected_weight_shape = (
              sample_shape
              + model.covar_module.batch_shape
              + (self.num_features,)
          )
          weight_generator = MagicMock(
              side_effect=lambda _: torch.rand(expected_weight_shape)
          )
          draw_kernel_feature_paths(
              model=model,
              sample_shape=sample_shape,
              num_features=self.num_features,
              weight_generator=weight_generator,
          )
          weight_generator.assert_called_once_with(
              expected_weight_shape
          )
  
imports: from __future__ import annotations
  from collections import defaultdict
  from copy import deepcopy
  from itertools import product
  from unittest.mock import MagicMock
  import torch
  from botorch.models import ModelListGP, SingleTaskGP, SingleTaskVariationalGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.sampling.pathwise import draw_kernel_feature_paths, GeneralizedLinearPath, PathList
  from botorch.sampling.pathwise.utils import get_train_inputs
  from botorch.utils.test_helpers import get_sample_moments, standardize_moments
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from torch import Size
  from torch.nn.functional import pad
label_desc: Use image node_id 2 for calling the TestPriorSamplers obj's underlying member method code with example usage: obj.test_draw_kernel_feature_paths() without return types