code: def noise(
      self, net: Tensor, weight: Tensor, use_noise: bool
  ) -> Tensor:
      if not use_noise:
          s = math.pow(self.layer + 1, 0.5)
          s = (
              s
              * torch.exp(-net * net / (2.0 * s * s))
              / math.sqrt(2 * math.pi)
              * 0.8
          )
          net = net + s
      else:
          if use_noise == "batch_constant":
              shape = 1, 1, net.shape[2], net.shape[3]
  
          else:
              shape = net.shape[0], 1, net.shape[2], net.shape[3]
          noise_tensor = torch.randn(*shape)
          net = net + weight * noise_tensor.to(weight.device)
      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 DecodeBlock obj's underlying member method code with example usage: obj.noise(net, weight, use_noise) and returns: net