code: def __init__(
      self,
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      submodule: nn.Module,
      up_kernel_size=3,
      strides=2,
      dropout=0.0,
  ):
      super().__init__()
      self.attention = AttentionBlock(
          spatial_dims=spatial_dims,
          f_g=in_channels,
          f_l=in_channels,
          f_int=in_channels // 2,
      )
      self.upconv = UpConv(
          spatial_dims=spatial_dims,
          in_channels=out_channels,
          out_channels=in_channels,
          strides=strides,
          kernel_size=up_kernel_size,
      )
      self.merge = Convolution(
          spatial_dims=spatial_dims,
          in_channels=2 * in_channels,
          out_channels=in_channels,
          dropout=dropout,
      )
      self.submodule = submodule
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers.factories import Norm
label_desc: Use image node_id 1 to create a new AttentionLayer object from inherited base classes: nn with example: obj = AttentionLayer(spatial_dims, in_channels, out_channels, submodule, up_kernel_size, strides, dropout)