code: def __init__(
      self,
      in_channels: int,
      latent_channels: int,
      num_blocks: int,
      *,
      deploy: bool = False,
      stride: int = 1,
      dilation: int = 1,
      use_post_se: bool = True,
  ):
      super().__init__()
      strides = [stride] + [1] * (num_blocks - 1)
      blocks = []
      in_nc = in_channels
      out_nc = latent_channels
      for stride in strides:
          blocks.append(
              RepVGGBlock(
                  in_nc,
                  out_nc,
                  deploy=deploy,
                  stride=stride,
                  dilation=dilation,
                  groups=1,
                  use_post_se=use_post_se,
              )
          )
          in_nc = out_nc
      self.net = nn.Sequential(*blocks)
  
imports: import torch
  import numpy
  import torch.nn
  from torch import Tensor
  from typing import Any
  from typing import List
  from typing import Tuple
  from typing import Optional
  from torchvision.models import vgg16
  from torchvision.models import vgg19
  from ..register import register_backbone
  from ......misc.toolkit import download_model
  from ......modules.blocks import SEBlock
label_desc: Use image node_id 1 to create a new RepVGGStage object from inherited base classes: nn with example: obj = RepVGGStage(in_channels, latent_channels, num_blocks)