code: def forward(ctx: Any, *args: Any, **kwargs: Any) -> Tensor:
      net, leaves = args
      softmax_leaves = F.softmax(leaves, dim=1)
      ctx.save_for_backward(net, softmax_leaves.t())
      return net.mm(softmax_leaves)
  
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 Dict
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Callable
  from typing import Optional
  from functools import partial
  from torch.nn import Module
  from cftool.misc import print_warning
  from .hooks import IBasicHook
label_desc: Use image node_id 1 for calling the LeafAggregation obj's underlying member method code with example usage: obj.forward(ctx) and returns: net