code: def make_batch(images):
      sample = np.random.randint(0, len(images), size=(batch_size))
      image_b = (
          images[sample].reshape(-1, 28 * 28).astype(np.float32) / 127.5
          - 1.0
      )
      return Tensor(image_b)
  
file_path: tinygrad/examples/mnist_gan.py
imports: from pathlib import Path
  import numpy
  from tqdm import trange
  import torch
  from torchvision.utils import make_grid, save_image
  from tinygrad.nn.state import get_parameters
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import getenv
  from tinygrad.nn import optim
  from extra.datasets import fetch_mnist
label_desc: Use image node_id 1 for calling a global function with example usage: make_batch(images) and returns: Tensor