code: def forward(self, x):
      shortcut = x
  
      # scales code re-written for torchscript as per my res2net fixes -rw
      # NOTE torch.split(x, self.width, 1) causing issues with ONNX export
      spx = x.chunk(len(self.convs) + 1, dim=1)
      spo = []
      sp = spx[0]
      for i, conv in enumerate(self.convs):
          if i > 0:
              sp = sp + spx[i]
          sp = conv(sp)
          spo.append(sp)
      spo.append(spx[-1])
      x = torch.cat(spo, 1)
  
      # XCA
      B, C, H, W = x.shape
      x = x.reshape(B, C, H * W).permute(0, 2, 1)
      if self.pos_embd is not None:
          pos_encoding = (
              self.pos_embd((B, H, W))
              .reshape(B, -1, x.shape[1])
              .permute(0, 2, 1)
          )
          x = x + pos_encoding
      x = x + self.drop_path(
          self.gamma_xca * self.xca(self.norm_xca(x))
      )
      x = x.reshape(B, H, W, C)
  
      # Inverted Bottleneck
      x = self.norm(x)
      x = self.mlp(x)
      if self.gamma is not None:
          x = self.gamma * x
      x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
  
      x = shortcut + self.drop_path(x)
      return x
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the SplitTransposeBlock obj's underlying member method code with example usage: obj.forward(x) and returns: x