code: def reductify_leaf(
      grad_input,
      grad_input_bdim,
      input_bdim,
      batch_size,
      target_shape_without_bdim_to_reduce_to=None,
  ):
      if grad_input is None:
          return None
  
      if grad_input_bdim is None and input_bdim is None:
          return grad_input
  
      if grad_input_bdim is not None and input_bdim is None:
          return grad_input.sum(grad_input_bdim)
  
      # NOTE: [Why can't we rely on autograd to reduce expanded gradients?]
      # For reverse-mode AD,
      # given a grad_input and input, it is valid for the user to return a
      # grad_input that has a broadcasted shape when compared to the input.
      # In this situation, autograd automatically reduces the grad_input to
      # the shape of the input.
      #
      # However, when input_bdim is not None, we have problems.
      #
      # [example 1]
      # grad_input: Tensor[3, 4], input: Tensor[B, 4]
      # We can expand grad_input to Tensor[B, 3, 4], but that isn't broadcastable
      # from [B, 4].
      #
      # [example 2]
      # grad_input: Tensor[3, B, 4], input: Tensor[B, 4]
      # We can swizzle grad_input to Tensor[B, 3, 4], but that isn't broadcastable
      # from [B, 4].
      #
      # This means that we need to also reduce the grad_input to the shape of the
      # input. This behavior is controlled by the `target_shape_without_bdim_to_reduce_to` flag;
      # if not-None then we do the reducing manually, otherwise, we do not do a reduction.
      assert input_bdim is not None
  
      if grad_input_bdim is None:
          grad_input = grad_input.unsqueeze(input_bdim)
          new_shape = list(grad_input.shape)
          new_shape[input_bdim] = batch_size
          grad_input = grad_input.expand(new_shape)
          grad_input_bdim = input_bdim
  
      if target_shape_without_bdim_to_reduce_to is not None:
          return vmap(
              torch.Tensor.sum_to_size,
              in_dims=(grad_input_bdim, None),
              out_dims=input_bdim,
          )(grad_input, target_shape_without_bdim_to_reduce_to)
  
      if input_bdim != grad_input_bdim:
          grad_input = grad_input.movedim(grad_input_bdim, input_bdim)
      return grad_input
  
imports: import torch
  from torch._ops import HigherOrderOperator
  from torch._C._functorch import TransformType
  from torch._functorch.utils import enable_single_level_autograd_function
  import torch.utils._pytree
  from torch._C._functorch import _wrap_for_grad, _unwrap_for_grad, current_level
  from torch._functorch.vmap import wrap_batched, unwrap_batched, restore_vmap, _add_batch_dim
  from torch._functorch.apis import vmap
  from torch._functorch.vmap import _broadcast_to_and_flatten
  from torch.autograd.forward_ad import _set_fwd_grad_enabled
  from typing import Any, NamedTuple, Tuple
label_desc: Use image node_id 12 for calling a global function with example usage: reductify_leaf(grad_input, grad_input_bdim, input_bdim, batch_size, target_shape_without_bdim_to_reduce_to) and returns: grad_input, None, grad_input, grad_input, vmap