code: def make_labels(bs, col, val=-2.0):
      y = np.zeros((bs, 2), np.float32)
      y[
          range(bs), [col] * bs
      ] = val  # Can we do label smoothing? i.e -2.0 changed to -1.98789.
      return Tensor(y)
  
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 2 for calling a global function with example usage: make_labels(bs, col, val) and returns: Tensor