code: def parse_training_data(
      consumer: Any,
      training_data: SupervisedDataset,
      **kwargs: Any,
  ) -> Dict[str, Tensor]:
      r"""Prepares a dataset for consumption by a given object.
  
      Args:
          training_datas: A SupervisedDataset.
          consumer: The object that will consume the parsed data, or type thereof.
  
      Returns:
          A dictionary containing the extracted information.
      """
      return dispatcher(consumer, training_data, **kwargs)
  
file_path: botorch/botorch/models/utils/parse_training_data.py
imports: from __future__ import annotations
  from typing import Any, Dict, Type
  import torch
  from botorch.models.model import Model
  from botorch.models.pairwise_gp import PairwiseGP
  from botorch.utils.datasets import RankingDataset, SupervisedDataset
  from botorch.utils.dispatcher import Dispatcher
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: parse_training_data(consumer, training_data) and returns: dispatcher