code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      gain: float = math.sqrt(2.0),
  ):
      super().__init__()
      self.mapping = Conv2d(
          in_channels, out_channels, kernel_size=1, gain=gain
      )
  
imports: import math
  import torch
  from typing import *
  from torch import nn
  from torch import Tensor
  from torch.nn import init
  from cftool.types import tensor_dict_type
  from .schema import Decoder1DMixin
  from ....constants import INPUT_KEY
  from ....constants import LABEL_KEY
  from ....constants import LATENT_KEY
  from ....misc.toolkit import auto_num_layers
  from ....modules.blocks import Conv2d
  from ....modules.blocks import Linear
  from ....modules.blocks import GaussianBlur3
  from ....modules.blocks import Activation
  from ....modules.blocks import NormFactory
  from ....modules.blocks import UpsampleConv2d
label_desc: Use image node_id 1 to create a new ToOutput object from inherited base classes: nn with example: obj = ToOutput(in_channels, out_channels, gain)