code: def forward(self, batch: tensor_dict_type) -> Tensor:
      styles = batch.get(INPUT_KEY)
      if styles is None:
          msg = "`styles` should be provided in batch.data in `StyleGenerator`"
          raise ValueError(msg)
  
      labels = batch.get(LABEL_KEY)
      condition = batch.get(LATENT_KEY)
      if condition is not None:
          net = condition
      elif not self.is_conditional:
          net = self.const
      else:
          if labels is None:
              msg = "Conditional StyleDecoder is defined but labels are not provided"
              raise ValueError(msg)
          net = self.const[labels.view(-1)]
  
      for i in range(self.num_upsample):
          net = self.blocks[i](net, styles, styles, self.noise)  # type: ignore
      net = self.to_output(net)
      return net
  
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 2 for calling the StyleDecoder obj's underlying member method code with example usage: obj.forward(batch) and returns: net