code: def evaluate(
      model,
      X_test,
      Y_test,
      num_classes=None,
      BS=128,
      return_predict=False,
      transform=lambda x: x,
      target_transform=lambda y: y,
  ):
      Tensor.training = False
  
      def numpy_eval(Y_test, num_classes):
          Y_test_preds_out = np.zeros(
              list(Y_test.shape) + [num_classes]
          )
          for i in trange((len(Y_test) - 1) // BS + 1, disable=CI):
              x = Tensor(transform(X_test[i * BS : (i + 1) * BS]))
              out = (
                  model.forward(x)
                  if hasattr(model, "forward")
                  else model(x)
              )
              Y_test_preds_out[i * BS : (i + 1) * BS] = out.numpy()
          Y_test_preds = np.argmax(Y_test_preds_out, axis=-1)
          Y_test = target_transform(Y_test)
          return (Y_test == Y_test_preds).mean(), Y_test_preds
  
      if num_classes is None:
          num_classes = Y_test.max().astype(int) + 1
      acc, Y_test_pred = numpy_eval(Y_test, num_classes)
      print("test set accuracy is %f" % acc)
      return (acc, Y_test_pred) if return_predict else acc
  
imports: import numpy
  from tqdm import trange
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import CI
  from tinygrad.jit import TinyJit
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, X_test, Y_test, num_classes, BS, return_predict, transform, target_transform) and returns: unknown, unknown,  Y_test_preds