code: def forward(
      self,
      net: Tensor,
      time_net: Tensor,
      context: Optional[Tensor] = None,
  ) -> Tensor:
      for layer in self:
          if isinstance(layer, TimestepBlock):
              net = layer(net, time_net)
          elif isinstance(layer, SpatialTransformer):
              net = layer(net, context)
          else:
              net = layer(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 1 for calling the TimestepAttnSequential obj's underlying member method code with example usage: obj.forward(net, time_net, context) and returns: net