code: def forward(self, x, feat_size: List[int]):
      x = self.fc1(x)
      B, N, C = x.shape
      x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1])
      x = self.relu(x)
      x = self.dwconv(x)
      x = x.flatten(2).transpose(1, 2)
      x = self.act(x)
      x = self.drop(x)
      x = self.fc2(x)
      x = self.drop(x)
      return x
  
imports: import math
  from typing import Tuple, List, Callable, Union
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the MlpWithDepthwiseConv obj's underlying member method code with example usage: obj.forward(x, feat_size) and returns: x