code: def forward(self, X):
      p = torch.distributions.Normal(0, 1)
      # sum over d dimensions
      return p.log_prob(X).sum(dim=-1).exp()
  
file_path: botorch/test/acquisition/test_prior_guided.py
imports: from itertools import product
  import torch
  from botorch.acquisition.analytic import ExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement
  from botorch.acquisition.prior_guided import PriorGuidedAcquisitionFunction
  from botorch.models import SingleTaskGP
  from botorch.utils.testing import BotorchTestCase
  from botorch.utils.transforms import match_batch_shape
  from torch.nn import Module
label_desc: Use image node_id 1 for calling the DummyPrior obj's underlying member method code with example usage: obj.forward(X) and returns: p