code: def forward(self, x):
      if self.training:
          B, H, W, C = x.shape
          r = torch.randperm(H * W)
          x = x.reshape(B, -1, C)
          x = x[:, r, :].reshape(B, H, W, -1)
      return x
  
imports: import math
  from functools import partial
  from itertools import accumulate
  from typing import Tuple
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the Shuffle obj's underlying member method code with example usage: obj.forward(x) and returns: x