code: def get_data(
      num_examples, input_size, data_type=np.asarray, include_label=True
  ):
      custom_create_test_image_3d = partial(
          create_test_image_3d,
          *input_size,
          rad_max=7,
          num_seg_classes=1,
          num_objs=1,
      )
      data = []
      for _ in range(num_examples):
          im, label = custom_create_test_image_3d()
          d = {}
          d["image"] = data_type(im)
          d["image_meta_dict"] = {"affine": np.eye(4)}
          if include_label:
              d["label"] = data_type(label)
              d["label_meta_dict"] = {"affine": np.eye(4)}
          d["label_transforms"] = []
          data.append(d)
      return data[0] if num_examples == 1 else data
  
file_path: MONAILabel/tests/unit/activelearning/test_epistemic.py
imports: import unittest
  from functools import partial
  import numpy
  import torch
  import tqdm
  from monai.data import CacheDataset, DataLoader, create_test_image_3d
  from monai.data.utils import pad_list_data_collate
  from monai.losses import DiceLoss
  from monai.networks.nets import UNet
  from monai.transforms import Compose, CropForeground, CropForegroundd, DivisiblePad, DivisiblePadd, EnsureChannelFirst, EnsureChannelFirstd
  from monai.utils import set_determinism
  from monailabel.tasks.scoring.epistemic import EpistemicScoring
label_desc: Use image node_id 1 for calling the TestEpistemicScoring obj's underlying member method code with example usage: obj.get_data(num_examples, input_size, data_type, include_label) and returns: unknown