code: def blob_label(y: np.ndarray, label: int, loc: List) -> np.ndarray:
      target = np.copy(y)  # type: ignore
      for l in loc:
          target[y == l] = label
      return target
  
imports: import logging
  import os
  from typing import Any, List, Tuple, Union
  import numpy
  import pytest
  from sklearn.datasets import make_blobs
  import torch
  from torch.cuda.amp.autocast_mode import autocast
  import torch.nn
  from torch.utils.data import DataLoader
  import torchvision
  import torchvision.transforms
  from fairscale.fair_dev.common_paths import DATASET_CACHE_ROOT
  from fairscale.fair_dev.testing.testing import skip_a_test_if_in_CI
  from fairscale.optim.layerwise_gradient_scaler import LayerwiseGradientScaler
label_desc: Use image node_id 1 for calling a global function with example usage: blob_label(y, label, loc) and returns: target