code: def forward(
      self,
      net: Tensor,
      *,
      timesteps: Tensor,
      context: Optional[Tensor] = None,
      labels: Optional[Tensor] = None,
      control: Optional[List[Tensor]] = None,
      only_mid_control: bool = False,
  ) -> Tensor:
      if (labels is None) ^ (self.num_classes is None):
          raise ValueError(
              "`labels` should be given iff `num_classes` is specified"
          )
  
      _before_forward(self, net, timesteps, context, False)
  
      # timenet
      time_net = timestep_embedding(
          timesteps,
          self.start_channels,
          dtype=net.dtype,
          repeat_only=False,
      )
      time_net = self.time_embedding(time_net)
      if self.label_embedding is not None:
          time_net = time_net + self.label_embedding(labels)
  
      # main
      nets = []
      for block in self.input_blocks:
          net = block(net, time_net, context)
          nets.append(net)
      net = self.residual(net, time_net, context)
      if control is not None:
          net += control.pop()
      for block in self.output_blocks:
          if only_mid_control or control is None:
              net = torch.cat([net, nets.pop()], dim=1)
          else:
              net = torch.cat([net, nets.pop() + control.pop()], dim=1)
          net = block(net, time_net, context)
      net = self.head(net)
  
      return net
  
imports: import math
  import torch
  from abc import abstractmethod
  from abc import ABCMeta
  from torch import nn
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Optional
  from ....modules.blocks import conv_nd
  from ....modules.blocks import zero_module
  from ....modules.blocks import HijackLinear
  from ....modules.blocks import SpatialTransformer
  from ....modules.blocks import MultiHeadSpatialAttention
  from ....modules.blocks import ResidualBlockWithTimeEmbedding
  from ....modules.blocks.mixed_stacks import walk_spatial_transformer_hooks
  from ....modules.blocks.convs.residual import ResUpsample
  from ....modules.blocks.convs.residual import ResDownsample
label_desc: Use image node_id 3 for calling the UNetDiffuser obj's underlying member method code with example usage: obj.forward(net) and returns: net