class_docstr: Modified Mlp containing depthwise convolution, based on a standard Mlp in ViT.
  
  Args:
      in_features (`int`): input dimension
      hidden_features (`int`): dimension used in depthwise conv layer
      out_features (`int`): output dimension
      act_layer (`nn.Module`): activation layer
      drop (`float`): dropout rate
class_docstr_tok: ["Modified","Mlp","containing","depthwise","convolution",",","based","on","a","standard","Mlp","in","ViT",".","Args",":","in_features","(","`","int","`",")",":","input","dimension","hidden_features","(","`","int","`",")",":","dimension","used","in","depthwise","conv","layer","out_features","(","`","int","`",")",":","output","dimension","act_layer","(","`","nn.Module","`",")",":","activation","layer","drop","(","`","float","`",")",":","dropout","rate"]
code: def forward(self, x, h, w):
      x = self.fc1(x)
      b, _, c = x.shape
  
      x0 = x.transpose(1, 2).view(b, c, h, w)
      x0 = self.dwconv(x0)
      x0 = x0.flatten(2).transpose(1, 2)
  
      x = self.act(x + x0)
      x = self.drop(x)
      x = self.fc2(x)
      x = self.drop(x)
      return x
  
imports: import torch
  from torch import nn
  from towhee.models.layers.patch_embed2d import PatchEmbed2D
  from towhee.models.layers.mlp import Mlp
  from towhee.models.layers.droppath import DropPath
  from towhee.models.utils.init_vit_weights import init_vit_weights
label_desc: Use image node_id 2 for calling the Mlp obj's underlying member method code with example usage: obj.forward(x, h, w) and returns: x