code: def forward(self, x):
      B, H, W, C = x.shape
  
      if self.with_vertical:
          v = x.permute(0, 2, 1, 3)
          v = v.reshape(-1, H, C)
          v, _ = self.rnn_v(v)
          v = v.reshape(B, W, H, -1)
          v = v.permute(0, 2, 1, 3)
      else:
          v = None
  
      if self.with_horizontal:
          h = x.reshape(-1, W, C)
          h, _ = self.rnn_h(h)
          h = h.reshape(B, H, W, -1)
      else:
          h = None
  
      if v is not None and h is not None:
          if self.union == "cat":
              x = torch.cat([v, h], dim=-1)
          else:
              x = v + h
      elif v is not None:
          x = v
      elif h is not None:
          x = h
  
      if self.fc is not None:
          x = self.fc(x)
  
      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 RNN2dBase obj's underlying member method code with example usage: obj.forward(x) and returns: x