code: def __init__(self, input_features: int, out_features: int) -> None:
      super().__init__(
          OrderedDict(
              [
                  ("relu1", nn.ReLU(inplace=True)),
                  (
                      "conv1",
                      nn.Conv2d(
                          input_features,
                          out_features,
                          kernel_size=3,
                          stride=1,
                          padding=2,
                          bias=True,
                      ),
                  ),
                  ("norm1", nn.BatchNorm2d(out_features)),
                  ("relu2", nn.ReLU(inplace=True)),
                  (
                      "conv2",
                      nn.Conv2d(
                          out_features,
                          out_features,
                          kernel_size=3,
                          stride=1,
                          bias=True,
                      ),
                  ),
                  ("norm2", nn.BatchNorm2d(out_features)),
              ]
          )
      )
  
imports: from __future__ import annotations
  from collections import OrderedDict
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor, concatenate
  from kornia.core.check import KORNIA_CHECK
  from kornia.utils import map_location_to_cpu
label_desc: Use image node_id 1 to create a new _DenseLayer object from inherited base classes: nn with example: obj = _DenseLayer(input_features, out_features)