code: def test_gen_kernel_features(self):
      for seed, kernel in enumerate(self.kernels):
          with torch.random.fork_rng():
              torch.random.manual_seed(seed)
              feature_map = gen_kernel_features(
                  kernel=kernel,
                  num_inputs=self.num_inputs,
                  num_outputs=self.num_features,
              )
  
              n = 4
              m = ceil(n * kernel.batch_shape.numel() ** -0.5)
              for input_batch_shape in (
                  (n**2,),
                  (m, *kernel.batch_shape, m),
              ):
                  X = torch.rand(
                      (*input_batch_shape, self.num_inputs),
                      device=kernel.device,
                      dtype=kernel.dtype,
                  )
                  self._test_gen_kernel_features(kernel, feature_map, X)
  
file_path: botorch/test/sampling/pathwise/features/test_generators.py
imports: from __future__ import annotations
  from math import ceil
  from unittest.mock import patch
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.sampling.pathwise.features import generators
  from botorch.sampling.pathwise.features.generators import gen_kernel_features
  from botorch.sampling.pathwise.features.maps import FeatureMap
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.kernels.kernel import Kernel
  from torch import Size, Tensor
label_desc: Use image node_id 2 for calling the TestFeatureGenerators obj's underlying member method code with example usage: obj.test_gen_kernel_features() without return types