code: def __init__(
      self,
      latent_dim: int,
      out_channels: int,
      latent_channels: int = 64,
      norm_type: Optional[str] = "instance",
      res_norm_type: Optional[str] = "instance",
      activation: Optional[str] = "leaky_relu_0.2",
      padding: str = "reflection",
      *,
      kernel_size: int = 3,
      last_kernel_size: Optional[int] = None,
      num_residual_blocks: int = 0,
      residual_dropout: float = 0.0,
      num_repeats: Union[str, List[int]] = "default",
      reduce_channel_on_upsample: bool = False,
      img_size: Optional[int] = None,
      num_upsample: Optional[int] = None,
      cond_channels: int = 16,
      num_classes: Optional[int] = None,
      latent_resolution: Optional[int] = None,
      latent_padding_channels: Optional[int] = None,
      final_activation: Optional[str] = None,
  ):
      super().__init__()
      self.latent_dim = latent_dim
      self._initialize(
          out_channels=out_channels,
          img_size=img_size,
          num_upsample=num_upsample,
          num_classes=num_classes,
          latent_resolution=latent_resolution,
      )
      if latent_resolution is None:
          latent_resolution = int(
              round(img_size / 2**self.num_upsample)
          )
      self.latent_resolution = latent_resolution
      assert isinstance(self.latent_resolution, int)
      latent_area = self.latent_resolution**2
      shape = -1, latent_channels, latent_resolution, latent_resolution
      blocks: List[nn.Module] = [
          Linear(latent_dim, latent_channels * latent_area),
          Activation.make(activation),
          Lambda(
              lambda tensor: tensor.view(*shape), f"reshape -> {shape}"
          ),
      ]
      if latent_padding_channels is not None:
          latent_padding = ChannelPadding(
              latent_channels,
              latent_padding_channels,
              latent_resolution,
          )
          blocks.append(latent_padding)
      self.from_latent = nn.Sequential(*blocks)
      self.decoder = VanillaDecoder(
          latent_channels,
          out_channels,
          norm_type,
          res_norm_type,
          activation,
          padding,
          kernel_size=kernel_size,
          last_kernel_size=last_kernel_size,
          num_residual_blocks=num_residual_blocks,
          residual_dropout=residual_dropout,
          num_repeats=num_repeats,
          reduce_channel_on_upsample=reduce_channel_on_upsample,
          img_size=img_size,
          num_upsample=self.num_upsample,
          cond_channels=cond_channels,
          num_classes=num_classes,
          latent_resolution=latent_resolution,
          final_activation=final_activation,
      )
  
imports: import torch.nn
  from torch import Tensor
  from typing import Any
  from typing import List
  from typing import Union
  from typing import Optional
  from cftool.misc import shallow_copy_dict
  from cftool.types import tensor_dict_type
  from .schema import DecoderMixin
  from .schema import Decoder1DMixin
  from ....constants import INPUT_KEY
  from ....modules.blocks import get_conv_blocks
  from ....modules.blocks import Lambda
  from ....modules.blocks import Linear
  from ....modules.blocks import Activation
  from ....modules.blocks import ResidualBlock
  from ....modules.blocks import ChannelPadding
  from ....modules.blocks import UpsampleConv2d
label_desc: Use image node_id 1 to create a new VanillaDecoder1D object from inherited base classes: nn, Decoder1DMixin with example: obj = VanillaDecoder1D(latent_dim, out_channels, latent_channels, norm_type, res_norm_type, activation, padding)