code: def _smooth_min(
      self, X: Tensor, dim: int, keepdim: bool = False
  ) -> Tensor:
      f = fatmin if self.fat else smooth_amin
      return f(X, tau=self.tau_max, dim=dim)
  
imports: from __future__ import annotations
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  from botorch.acquisition.logei import TAU_MAX, TAU_RELU
  from botorch.acquisition.multi_objective import MultiObjectiveMCAcquisitionFunction
  from botorch.acquisition.multi_objective.objective import MCMultiOutputObjective
  from botorch.models.model import Model
  from botorch.sampling.base import MCSampler
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.multi_objective.hypervolume import NoisyExpectedHypervolumeMixin, SubsetIndexCachingMixin
  from botorch.utils.objective import compute_smoothed_feasibility_indicator
  from botorch.utils.safe_math import fatmin, log_fatplus, log_softplus, logdiffexp, logmeanexp, logplusexp, logsumexp, smooth_amin
  from botorch.utils.transforms import concatenate_pending_points, is_ensemble, match_batch_shape, t_batch_mode_transform
  from torch import Tensor
label_desc: Use image node_id 6 for calling the qLogExpectedHypervolumeImprovement obj's underlying member method code with example usage: obj._smooth_min(X, dim, keepdim) and returns: f