code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      latent_dim: int,
      resolution: int,
      kernel_size: int = 3,
      upscale_factor: float = 1.0,
      use_noise: bool = True,
      activation: str = "leaky_relu_0.2",
      activation_gain: float = math.sqrt(2.0),
      resample_filter: Optional[List[int]] = None,
      conv_clamp: Optional[float] = None,
  ):
      super().__init__()
      if resample_filter is None:
          resample_filter = [1, 3, 3, 1]
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.resolution = resolution
      self.upscale_factor = upscale_factor
      self.use_noise = use_noise
      self.activation = Activation.make(activation)
      self.activation_gain = activation_gain
      self.conv_clamp = conv_clamp
      self.register_buffer(
          "resample_filter", setup_filter(resample_filter)
      )
      self.affine = FullyConnected(
          latent_dim, in_channels, bias_init=1.0
      )
      self.conv = UpsampleConv2d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          mode="transpose",
          bias=False,
          demodulate=True,
          factor=upscale_factor,
      )
      if use_noise:
          self.register_buffer(
              "noise_const", torch.randn([resolution, resolution])
          )
          self.noise_strength = nn.Parameter(torch.tensor(0.0))
      self.bias = nn.Parameter(torch.zeros([1, out_channels, 1, 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 StyleGAN2Conv object from inherited base classes: nn with example: obj = StyleGAN2Conv(in_channels, out_channels, latent_dim, resolution, kernel_size, upscale_factor, use_noise, activation, activation_gain, resample_filter, conv_clamp)