code: def make_dataset():
      ds = []
      for i in range(100):
          for j in range(100):
              s = i + j
              ds.append(
                  [
                      i // 10,
                      i % 10,
                      j // 10,
                      j % 10,
                      s // 100,
                      (s // 10) % 10,
                      s % 10,
                  ]
              )
      random.shuffle(ds)
      ds = np.array(ds).astype(np.float32)
      ds_X = ds[:, 0:6]
      ds_Y = np.copy(ds[:, 1:])
      ds_X_train, ds_X_test = ds_X[0:8000], ds_X[8000:]
      ds_Y_train, ds_Y_test = ds_Y[0:8000], ds_Y[8000:]
      return ds_X_train, ds_Y_train, ds_X_test, ds_Y_test
  
imports: import numpy
  import random
  from tinygrad.nn.state import get_parameters
  from tinygrad.nn.optim import Adam
  from extra.training import train, evaluate
  from extra.models.transformer import Transformer
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset() and returns: ds_X_train,  ds_Y_train,  ds_X_test,  ds_Y_test