code: def load_data(model_type: str) -> Union[DataLoader, Tuple[Any, Any]]:
      data = None
      if model_type == "linear_model":
          torch.manual_seed(11)
          x_train, y_train = make_blobs(
              n_samples=40,
              n_features=2,
              cluster_std=1.5,
              shuffle=True,
              random_state=10,
          )
          x_train = torch.FloatTensor(x_train)
          y_train = torch.FloatTensor(blob_label(y_train, 0, [0]))
          y_train = torch.FloatTensor(blob_label(y_train, 1, [1, 2, 3]))
  
          data = (x_train, y_train)
  
      if model_type == "vision_model":
          torch.manual_seed(10)
          transform = transforms.Compose(
              [
                  transforms.ToTensor(),
                  transforms.Normalize(
                      (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
                  ),
              ]
          )
  
          # TODO: we should NOT do this download over and over again during test.
          train_ds = torchvision.datasets.CIFAR10(
              root=DATASET_CACHE_ROOT,
              train=True,
              download=True,
              transform=transform,
          )
          train_ds_loader = torch.utils.data.DataLoader(
              train_ds, batch_size=128, shuffle=False, num_workers=2
          )
  
          image, _ = train_ds[0]
          assert image.shape == torch.Size([3, 32, 32])
          data = train_ds_loader  # type: ignore
      return data
  
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 2 for calling a global function with example usage: load_data(model_type) and returns: data