code: def make_dataset(
      num_samples: int = 3,
      d: int = 2,
      m: int = 1,
      has_yvar: bool = False,
      feature_names: Optional[List[str]] = None,
      outcome_names: Optional[List[str]] = None,
      batch_shape: Optional[torch.Size] = None,
  ) -> SupervisedDataset:
      feature_names = feature_names or [f"x{i}" for i in range(d)]
      outcome_names = outcome_names or [f"y{i}" for i in range(m)]
      batch_shape = batch_shape or torch.Size()
      return SupervisedDataset(
          X=rand(*batch_shape, num_samples, d),
          Y=rand(*batch_shape, num_samples, m),
          Yvar=rand(*batch_shape, num_samples, m) if has_yvar else None,
          feature_names=feature_names,
          outcome_names=outcome_names,
      )
  
file_path: botorch/test/utils/test_datasets.py
imports: from typing import List, Optional
  import torch
  from botorch.exceptions.errors import InputDataError, UnsupportedError
  from botorch.utils.containers import DenseContainer, SliceContainer
  from botorch.utils.datasets import ContextualDataset, FixedNoiseDataset, MultiTaskDataset, RankingDataset, SupervisedDataset
  from botorch.utils.testing import BotorchTestCase
  from torch import rand, randperm, Size, stack, Tensor, tensor
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset(num_samples, d, m, has_yvar, feature_names, outcome_names, batch_shape) and returns: SupervisedDataset