code: def setUp(self, seed: int = 0) -> None:
      super().setUp()
  
      self.kernels = []
      self.num_inputs = d = 2
      self.num_features = 4096
      for kernel in (
          MaternKernel(nu=0.5, batch_shape=Size([])),
          MaternKernel(nu=1.5, ard_num_dims=1, active_dims=[0]),
          ScaleKernel(
              MaternKernel(
                  nu=2.5, ard_num_dims=d, batch_shape=Size([2])
              )
          ),
          ScaleKernel(
              RBFKernel(ard_num_dims=1, batch_shape=Size([2, 2])),
              active_dims=[1],
          ),
      ):
          kernel.to(
              dtype=torch.float32 if (seed % 2) else torch.float64,
              device=self.device,
          )
          with torch.random.fork_rng():
              torch.manual_seed(seed)
              kern = (
                  kernel.base_kernel
                  if isinstance(kernel, ScaleKernel)
                  else kernel
              )
              kern.lengthscale = 0.1 + 0.2 * torch.rand_like(
                  kern.lengthscale
              )
              seed += 1
  
          self.kernels.append(kernel)
  
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 1 for calling the TestFeatureGenerators obj's underlying member method code with example usage: obj.setUp(seed) without return types