code: def reductify(
      grad_input,
      grad_input_bdim,
      input_bdim,
      batch_size,
      target_shape_without_bdim_to_reduce_to=None,
  ):
      if not isinstance(grad_input, tuple):
          grad_input = (grad_input,)
      if not isinstance(grad_input_bdim, tuple):
          grad_input_bdim = (grad_input_bdim,)
      if not isinstance(input_bdim, tuple):
          input_bdim = (input_bdim,)
  
      if target_shape_without_bdim_to_reduce_to is None:
          target_shape_without_bdim_to_reduce_to = len(grad_input) * (
              None,
          )
      result = tuple(
          reductify_leaf(gi, gi_bdim, i_bdim, batch_size, maybe_ishape)
          for gi, gi_bdim, i_bdim, maybe_ishape in zip(
              grad_input,
              grad_input_bdim,
              input_bdim,
              target_shape_without_bdim_to_reduce_to,
          )
      )
      return result
  
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 11 for calling a global function with example usage: reductify(grad_input, grad_input_bdim, input_bdim, batch_size, target_shape_without_bdim_to_reduce_to) and returns: result