code: def repeat_to_match_aug_dim(
      target_tensor: Tensor, reference_tensor: Tensor
  ) -> Tensor:
      """Repeat target_tensor until it has the same first dimension as reference_tensor
      This works regardless of the batch shapes and q.
      This is useful as we sometimes modify sample shapes such as in LearnedObjective.
  
      Args:
          target_tensor: A `sample_size x batch_shape x q x m`-dim Tensor
          reference_tensor: A `(augmented_sample * sample_size) x batch_shape x q`-dim
              Tensor. `augmented_sample` could be 1.
  
      Returns:
          The content of `target_tensor` potentially repeated so that its first dimension
          matches that of `reference_tensor`.
          The shape will be `(augmented_sample * sample_size) x batch_shape x q x m`.
  
      Examples:
          >>> import torch
          >>> target_tensor = torch.arange(3).repeat(2, 1).T
          >>> target_tensor
          tensor([[0, 0],
                  [1, 1],
                  [2, 2]])
          >>> repeat_to_match_aug_dim(target_tensor, torch.zeros(6))
          tensor([[0, 0],
                  [1, 1],
                  [2, 2],
                  [0, 0],
                  [1, 1],
                  [2, 2]])
      """
      augmented_sample_num, remainder = divmod(
          reference_tensor.shape[0], target_tensor.shape[0]
      )
      if remainder != 0:
          raise ValueError(
              "The first dimension of reference_tensor must "
              "be a multiple of target_tensor's."
          )
  
      # using repeat here as obj might be constructed as
      # obj.reshape(-1, *samples.shape[2:]) where the first 2 dimensions are
      # of shape `augmented_samples x sample_shape`.
      repeat_size = (augmented_sample_num,) + (1,) * (
          target_tensor.ndim - 1
      )
      return target_tensor.repeat(*repeat_size)
  
imports: from __future__ import annotations
  import math
  from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from botorch.acquisition.objective import IdentityMCObjective, MCAcquisitionObjective, PosteriorTransform
  from botorch.exceptions.errors import BotorchTensorDimensionError, DeprecationError, UnsupportedError
  from botorch.models.fully_bayesian import MCMC_DIM
  from botorch.models.model import Model
  from botorch.sampling.base import MCSampler
  from botorch.sampling.get_sampler import get_sampler
  from botorch.sampling.pathwise import draw_matheron_paths
  from botorch.utils.objective import compute_feasibility_indicator
  from botorch.utils.sampling import optimize_posterior_samples
  from botorch.utils.transforms import is_ensemble, normalize_indices
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: repeat_to_match_aug_dim(target_tensor, reference_tensor) and returns: target_tensor