class_docstr: Pairwise likelihood using logistic (i.e., sigmoid) function
  
  Given two items v and u with utilities f(v) and f(u), the probability that we
  prefer v over u with probability sigmoid(f(v) - f(u)). Note
  that this formulation implicitly assume the beta term in logistic function is
  fixed at 1.
code: def log_p(self, utility: Tensor, D: Tensor) -> Tensor:
      logit = self._calc_logit(utility=utility, D=D)
      return torch.nn.functional.logsigmoid(logit)
  
imports: from __future__ import annotations
  import math
  from abc import ABC, abstractmethod
  from typing import Any, Tuple
  import torch
  from botorch.utils.probability.utils import log_ndtr, log_phi, standard_normal_log_hazard
  from gpytorch.likelihoods import Likelihood
  from torch import Tensor
  from torch.distributions import Bernoulli
label_desc: Use image node_id 2 for calling the PairwiseLogitLikelihood obj's underlying member method code with example usage: obj.log_p(utility, D) and returns: torch