code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      latent_dim: int,
      resolution: int,
      img_channels: int,
      is_last: bool,
      architecture: str = "skip",
      resample_filter: Optional[List[int]] = None,
      conv_clamp: Optional[float] = None,
      **conv_kwargs: Any,
  ):
      super().__init__()
      if resample_filter is None:
          resample_filter = [1, 3, 3, 1]
      self.in_channels = in_channels
      self.w_dim = latent_dim
      self.resolution = resolution
      self.img_channels = img_channels
      self.is_last = is_last
      self.architecture = architecture
      self.register_buffer(
          "resample_filter", setup_filter(resample_filter)
      )
      self.num_conv = 0
      self.num_to_rgb = 0
  
      if in_channels == 0:
          self.conv0 = None
      else:
          self.conv0 = StyleGAN2Conv(
              in_channels,
              out_channels,
              latent_dim=latent_dim,
              resolution=resolution,
              upscale_factor=2.0,
              resample_filter=resample_filter,
              conv_clamp=conv_clamp,
              **conv_kwargs,
          )
          self.num_conv += 1
  
      self.conv1 = StyleGAN2Conv(
          out_channels,
          out_channels,
          latent_dim=latent_dim,
          resolution=resolution,
          conv_clamp=conv_clamp,
          **conv_kwargs,
      )
      self.num_conv += 1
  
      if not (is_last or architecture == "skip"):
          self.to_rgb = None
      else:
          self.to_rgb = ToRGB(
              out_channels,
              img_channels,
              w_dim=latent_dim,
              conv_clamp=conv_clamp,
          )
          self.num_to_rgb += 1
  
imports: import math
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import List
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .schema import Decoder1DMixin
  from ....constants import INPUT_KEY
  from ....constants import LABEL_KEY
  from ....modules.blocks import Conv2d
  from ....modules.blocks import Activation
  from ....modules.blocks import UpsampleConv2d
label_desc: Use image node_id 1 to create a new StyleGAN2Block object from inherited base classes: nn with example: obj = StyleGAN2Block(in_channels, out_channels, latent_dim, resolution, img_channels, is_last, architecture, resample_filter, conv_clamp)