code: def forward(self, X: Tensor) -> Tensor:
      X_full = torch.cat(
          [match_batch_shape(self.X_baseline, X), X], dim=-2
      )
      # NOTE: To ensure that we correctly sample `f(X)` from the joint distribution
      # `f((X_baseline, X)) ~ P(f | D)`, it is critical to compute the joint posterior
      # over X *and* X_baseline -- which also contains pending points whenever there
      # are any --  since the baseline and pending values `f(X_baseline)` are
      # generally pre-computed and cached before the `forward` call, see the docs of
      # `cache_pending` for details.
      # TODO: Improve the efficiency by not re-computing the X_baseline-X_baseline
      # covariance matrix, but only the covariance of
      # 1) X and X, and
      # 2) X and X_baseline.
      posterior = self.model.posterior(X_full)
      # Account for possible one-to-many transform and the MCMC batch dimension in
      # `SaasFullyBayesianSingleTaskGP`
      event_shape_lag = 1 if is_ensemble(self.model) else 2
      n_w = (
          posterior._extended_shape()[X_full.dim() - event_shape_lag]
          // X_full.shape[-2]
      )
      q_in = X.shape[-2] * n_w
      self._set_sampler(q_in=q_in, posterior=posterior)
      samples = self._get_f_X_samples(posterior=posterior, q_in=q_in)
      # Add previous nehvi from pending points.
      return (
          self._compute_qehvi(samples=samples, X=X) + self._prev_nehvi
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Callable, List, Optional, Union
  import torch
  from botorch.acquisition.acquisition import AcquisitionFunction, MCSamplerMixin
  from botorch.acquisition.multi_objective.objective import IdentityMCMultiOutputObjective, MCMultiOutputObjective
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.model import Model
  from botorch.models.transforms.input import InputPerturbation
  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.transforms import concatenate_pending_points, is_ensemble, match_batch_shape, t_batch_mode_transform
  from torch import Tensor
label_desc: Use image node_id 2 for calling the qNoisyExpectedHypervolumeImprovement obj's underlying member method code with example usage: obj.forward(X) and returns: unknown