code: def _reshape_base_samples(
      base_samples: Tensor,
      sample_shape: torch.Size,
      posterior: GPyTorchPosterior,
  ) -> Tensor:
      r"""Manipulate shape of base_samples to match `MultivariateNormal.rsample`.
  
      This ensure that base_samples are used in the same way as in
      gpytorch.distributions.MultivariateNormal. For CBD, it is important to ensure
      that the same base samples are used for the in-sample points here and in the
      cached box decompositions.
  
      Args:
          base_samples: The base samples.
          sample_shape: The sample shape.
          posterior: The joint posterior is over (X_baseline, X).
  
      Returns:
          Reshaped and expanded base samples.
      """
      mvn = posterior.distribution
      loc = mvn.loc
      peshape = posterior._extended_shape()
      base_samples = base_samples.view(
          sample_shape + torch.Size([1] * (loc.ndim - 1)) + peshape[-2:]
      ).expand(sample_shape + loc.shape[:-1] + peshape[-2:])
      if posterior._is_mt:
          base_samples = _reshape_base_samples_non_interleaved(
              mvn=posterior.distribution,
              base_samples=base_samples,
              sample_shape=sample_shape,
          )
      base_samples = base_samples.reshape(
          -1, *loc.shape[:-1], mvn.lazy_covariance_matrix.shape[-1]
      )
      base_samples = base_samples.permute(*range(1, loc.dim() + 1), 0)
      return base_samples.reshape(
          *peshape[:-2],
          peshape[-1],
          peshape[-2],
          *sample_shape,
      )
  
file_path: botorch/botorch/utils/low_rank.py
imports: from __future__ import annotations
  import torch
  from botorch.exceptions.errors import BotorchError
  from botorch.posteriors.base_samples import _reshape_base_samples_non_interleaved
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from gpytorch.distributions.multitask_multivariate_normal import MultitaskMultivariateNormal
  from linear_operator.operators import BlockDiagLinearOperator, LinearOperator
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from linear_operator.utils.errors import NanError
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: _reshape_base_samples(base_samples, sample_shape, posterior) and returns: base_samples