code: def __call__(self, x, training=True):
      # pad to 32x32 because whitening conv creates 31x31 images that are awfully slow to compute with
      # TODO: remove the pad but instead let the kernel optimize itself
      forward = (
          lambda x: x.conv2d(self.whitening)
          .pad2d((1, 0, 0, 1))
          .sequential(self.net)
      )
      return (
          forward(x)
          if training
          else forward(x) * 0.5 + forward(x[..., ::-1]) * 0.5
      )
  
file_path: tinygrad/examples/hlb_cifar10.py
imports: import random, time
  import numpy
  from typing import Optional
  from extra.datasets import fetch_cifar, cifar_mean, cifar_std
  from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit
  from tinygrad.nn.state import get_state_dict, get_parameters
  from tinygrad.nn import optim
  from extra.lr_scheduler import OneCycleLR
  from tinygrad.helpers import Context, getenv
label_desc: Use image node_id 2 for calling the SpeedyResNet obj's underlying member method code with example usage: obj.__call__(x, training) and returns: unknown