class_docstr: Divider & encoder
  
  dim (`int`):
      Dimension of features
  h (`int`):
      Height of input.
  u (`int`):
      Parameter to multiple linear dimension.
class_docstr_tok: ["Divider","&","encoder","dim","(","`","int","`",")",":","Dimension","of","features","h","(","`","int","`",")",":","Height","of","input",".","u","(","`","int","`",")",":","Parameter","to","multiple","linear","dimension","."]
code: def forward(self, x, norm=True):
      x = x.reshape([-1, self.h, 1])
      x = self.linear1(x)
      x = self.elu(x)
      x = self.linear2(x)
      x = x.reshape([-1, self.d])
      if norm:
          x = torch.nn.functional.normalize(x, p=2.0)
      return x
  
file_path: towhee/towhee/models/nnfp/nnfp.py
imports: import torch
  from torch import nn
  from towhee.models.layers.conv2d_separable import SeparableConv2d
label_desc: Use image node_id 2 for calling the DivEncoder obj's underlying member method code with example usage: obj.forward(x, norm) and returns: x