code: def forward(self, x):
      shortcut = x
      x = self.conv_dw(x)
      if self.shortcut_after_dw:
          shortcut = x
  
      x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
      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 ConvBlock obj's underlying member method code with example usage: obj.forward(x) and returns: x