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 __init__(self, dim, h, u):
      super().__init__()
      assert h % dim == 0, f"h ({h}) must be divisible by d ({dim})"
      v = h // dim
      self.d = dim
      self.h = h
      self.u = u
      self.v = v
      self.linear1 = nn.Conv1d(
          dim * v, dim * u, kernel_size=(1,), groups=dim
      )
      self.elu = nn.ELU()
      self.linear2 = nn.Conv1d(
          dim * u, dim, kernel_size=(1,), groups=dim
      )
  
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 1 to create a new DivEncoder object from inherited base classes: nn with example: obj = DivEncoder(dim, h, u)