class_docstr: RepMLP Unit (composed of RepMLP block)
  
  Args:
      channels (`int`):
          Number of input channels & final output channels.
      internal_neurons (`int`):
          Number of channels used to connect conv2d layers inside block.
      h (`int`):
          Input image height.
      w (`int`):
          Input image weight.
      reparam_conv_k (`tuple`):
          Numbers of conv layers.
      globalperceptron_reduce (`int`):
          Number to reduce internal hidden channels.
      ffn_expand (`int`):
          Number to expan channels in FFN block
      num_sharesets (`int`):
          Number of sharesets.
      deploy (`bool`):
          Flag to control deploy parameters like bias.
class_docstr_tok: ["RepMLP","Unit","(","composed","of","RepMLP","block",")","Args",":","channels","(","`","int","`",")",":","Number","of","input","channels","&","final","output","channels",".","internal_neurons","(","`","int","`",")",":","Number","of","channels","used","to","connect","conv2d","layers","inside","block",".","h","(","`","int","`",")",":","Input","image","height",".","w","(","`","int","`",")",":","Input","image","weight",".","reparam_conv_k","(","`","tuple","`",")",":","Numbers","of","conv","layers",".","globalperceptron_reduce","(","`","int","`",")",":","Number","to","reduce","internal","hidden","channels",".","ffn_expand","(","`","int","`",")",":","Number","to","expan","channels","in","FFN","block","num_sharesets","(","`","int","`",")",":","Number","of","sharesets",".","deploy","(","`","bool","`",")",":","Flag","to","control","deploy","parameters","like","bias","."]
code: def __init__(
      self,
      channels,
      h,
      w,
      reparam_conv_k,
      globalperceptron_reduce,
      ffn_expand=4,
      num_sharesets=1,
      deploy=False,
  ):
      super().__init__()
      self.repmlp_block = RepMLPBlock(
          in_channels=channels,
          out_channels=channels,
          h=h,
          w=w,
          reparam_conv_k=reparam_conv_k,
          globalperceptron_reduce=globalperceptron_reduce,
          num_sharesets=num_sharesets,
          deploy=deploy,
      )
      self.ffn_block = FFNBlock(channels, channels * ffn_expand)
      self.prebn1 = nn.BatchNorm2d(channels)
      self.prebn2 = nn.BatchNorm2d(channels)
  
file_path: towhee/towhee/models/repmlp/repmlp.py
imports: from torch import nn
  import torch.utils.checkpoint
  from towhee.models.layers.ffn import FFNBlock
  from towhee.models.layers.conv_bn_activation import Conv2dBNActivation
  from towhee.models.utils import create_model
  from .blocks import RepMLPBlock
  from .configs import get_configs
label_desc: Use image node_id 1 to create a new RepMLPNetUnit object from inherited base classes: nn with example: obj = RepMLPNetUnit(channels, h, w, reparam_conv_k, globalperceptron_reduce, ffn_expand, num_sharesets, deploy)