code: def fetch_batch(bs, val=False):
      cir = get_imagenet_categories()
      files = get_val_files() if val else get_train_files()
      samp = np.random.randint(0, len(files), size=(bs))
      files = [files[i] for i in samp]
      X = [image_load(x) for x in files]
      Y = [cir[x.split("/")[0]] for x in files]
      return np.array(X), np.array(Y)
  
file_path: tinygrad/extra/datasets/imagenet.py
imports: import glob, random, json
  import numpy
  from PIL import Image
  import functools, pathlib
  from tinygrad.helpers import DEBUG, diskcache
label_desc: Use image node_id 6 for calling a global function with example usage: fetch_batch(bs, val) and returns: np,  np