code: def __init__(self, in_channels: int, out_channels: int):
      super().__init__()
      self.conv_bn_relu = nn.Sequential(
          DepthwiseSeparableConv2d(
              in_channels + out_channels,
              out_channels,
              kernel_size=3,
              padding=1,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(inplace=True),
      )
  
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 AMM object from inherited base classes: nn with example: obj = AMM(in_channels, out_channels)