code: def __init__(self, in_channels: int, out_channels: int):
      super().__init__()
      self.conv1 = nn.Conv2d(
          in_channels, out_channels, kernel_size=1, bias=False
      )
  
      self.block = nn.Sequential(
          nn.Conv2d(
              out_channels,
              out_channels,
              kernel_size=3,
              padding=1,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              out_channels, out_channels, kernel_size=3, padding=1
          ),
      )
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..dsconv import DepthwiseSeparableConv2d
  from ..interfaces import AbstractDecoder, FeatureMapsSpecification
label_desc: Use image node_id 1 to create a new RCM object from inherited base classes: nn with example: obj = RCM(in_channels, out_channels)