code: def _batch_blobs(num_batches, num_samples, num_features, num_classes):
      data, labels = [], []
      for _ in range(num_batches):
          _data, _labels = make_blobs(
              num_samples, num_features, centers=num_classes
          )
          data.append(torch.tensor(_data))
          labels.append(torch.tensor(_labels))
  
      return _IntrinsicInput(
          data=torch.stack(data), labels=torch.stack(labels)
      )
  
file_path: torchmetrics/tests/unittests/clustering/inputs.py
imports: from typing import NamedTuple
  import torch
  from sklearn.datasets import make_blobs
  from torch import Tensor
  from unittests import BATCH_SIZE, EXTRA_DIM, NUM_BATCHES, NUM_CLASSES, _Input
  from unittests.helpers import seed_all
label_desc: Use image node_id 1 for calling a global function with example usage: _batch_blobs(num_batches, num_samples, num_features, num_classes) and returns: _IntrinsicInput