code: def __init__(
      self,
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      act: tuple[str, dict] | str,
      bias: bool = False,
  ):
      super().__init__()
  
      conv_type: type[nn.Conv2d | nn.Conv3d] = Conv[
          Conv.CONV, spatial_dims
      ]
  
      self.act_function1 = get_acti_layer(act, out_channels)
      self.conv_block = Convolution(
          spatial_dims=spatial_dims,
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=5,
          act=None,
          norm=Norm.BATCH,
          bias=bias,
      )
      self.conv2 = conv_type(out_channels, out_channels, kernel_size=1)
  
imports: from __future__ import annotations
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers.factories import Act, Conv, Dropout, Norm, split_args
  from monai.utils import deprecated_arg
label_desc: Use image node_id 1 to create a new OutputTransition object from inherited base classes: nn with example: obj = OutputTransition(spatial_dims, in_channels, out_channels, act, bias)