code: def logdiffexp(log_a: Tensor, log_b: Tensor) -> Tensor:
      """Computes log(b - a) accurately given log(a) and log(b).
      Assumes, log_b > log_a, i.e. b > a > 0.
  
      Args:
          log_a (Tensor): The logarithm of a, assumed to be less than log_b.
          log_b (Tensor): The logarithm of b, assumed to be larger than log_a.
  
      Returns:
          A Tensor of values corresponding to log(b - a).
      """
      log_a, log_b = torch.broadcast_tensors(log_a, log_b)
      is_inf = (
          log_b == -torch.inf
      )  # implies log_a == -torch.inf by assumption
      return log_b + log1mexp(log_a - log_b.masked_fill(is_inf, 0.0))
  
imports: from __future__ import annotations
  import math
  from typing import Callable, Tuple, Union
  import torch
  from botorch.exceptions import UnsupportedError
  from botorch.utils.constants import get_constants_like
  from torch import finfo, Tensor
  from torch.nn.functional import softplus
label_desc: Use image node_id 11 for calling a global function with example usage: logdiffexp(log_a, log_b) and returns: unknown