code: def forward(
      self,
      net: Tensor,
      w: Tensor,
      noise_mode: str = "random",
      gain: float = 1.0,
  ) -> Tensor:
      styles = self.affine(w)
      net = self.conv(net, styles)
      if self.upscale_factor > 1.0:
          net = resample(
              net,
              self.resample_filter,
              1.0,
              [1] * 4,
              self.upscale_factor**2,
          )
      if self.use_noise:
          if noise_mode == "const":
              noise = self.noise_const
          elif noise_mode == "random":
              noise_shape = (
                  net.shape[0],
                  1,
                  self.resolution,
                  self.resolution,
              )
              noise = torch.randn(noise_shape, device=net.device)
          else:
              raise NotImplementedError(
                  f"noise_mode '{noise_mode}' not implemented"
              )
          noise = noise * self.noise_strength
          net.add_(noise)
      net = net + self.bias
      activation_gain = self.activation_gain * gain
      if activation_gain != 1.0:
          net = net * activation_gain
      net = self.activation(net)
      if self.conv_clamp is not None:
          conv_clamp = self.conv_clamp * gain
          net = torch.clamp(net, -conv_clamp, conv_clamp)
      return net
  
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 2 for calling the StyleGAN2Conv obj's underlying member method code with example usage: obj.forward(net, w, noise_mode, gain) and returns: net