code: def vmapify_autograd_function(
      autograd_function, in_dims, batch_size, randomness
  ):
      # The following values are saved from the forward() and setup_context()
      # and used in backward().
      # Why do we save the values out here instead of on the ctx object?
      # - out_dims: There's no way to retrieve this from forward()
      # - input_shapes, saved_tensors_bdims: I'm a bit scared of nesting
      #   vmap(vmap( but not completely sure if it is a problem. If we
      #   assigned those fields to the ctx object, the worry is that they
      #   get overwritten.
      init_val = "not populated"
      out_dims = init_val
      input_shapes: Any = init_val
      saved_tensors_bdims: Any = init_val
  
      def forward(*operands):
          nonlocal out_dims
          outputs, out_dims = restore_vmap(
              autograd_function.forward, in_dims, batch_size, randomness
          )(*operands)
          return outputs
  
      def setup_context(ctx, inputs, outputs):
          input_shapes_ = None
          saved_tensors_bdims_ = None
  
          def inner(inputs, outputs):
              # wrapped_ctx.save_for_backward will:
              # - unwrap batchedtensors into (tensor, bdim)
              # - save_for_backward(*unwrapped_tensors)
              # - assign the bdims to wrapped_ctx._pt_saved_tensors_bdims
              wrapped_ctx = CtxCustomSave(ctx, current_level())
              autograd_function.setup_context(
                  wrapped_ctx, inputs, outputs
              )
  
              # input_shapes are used for reductify later to reduce expanded gradients
              # to the correct shape.
              # See NOTE: [Why can't we rely on autograd to reduce expanded gradients?]
              # for more details
              nonlocal input_shapes_
              input_shapes_ = tuple(
                  inp.shape if isinstance(inp, torch.Tensor) else None
                  for inp in inputs
              )
              nonlocal saved_tensors_bdims_
              saved_tensors_bdims_ = wrapped_ctx._pt_saved_tensors_bdims
  
          # See NOTE: [Why do we need to run setup_context under a vmap?]
          restore_vmap(
              inner,
              (in_dims, out_dims),
              batch_size,
              randomness,
          )(inputs, outputs)
  
          nonlocal input_shapes
          input_shapes = input_shapes_
          nonlocal saved_tensors_bdims
          saved_tensors_bdims = saved_tensors_bdims_
  
      def jvp(ctx, *tangents):
          assert out_dims != init_val
          assert saved_tensors_bdims != init_val
  
          def jvp_no_context(saved_tensors, tangents):
              wrapped_ctx = CtxWithSavedTensors(ctx, saved_tensors)
              return autograd_function.jvp(wrapped_ctx, *tangents)
  
          tangent_in_dims = get_tangents_in_dims(in_dims, tangents)
          out_tangents, out_tangents_dims = restore_vmap(
              jvp_no_context,
              (saved_tensors_bdims, tangent_in_dims),
              batch_size,
              randomness,
          )(ctx.saved_tensors, tangents)
  
          result = reductify(
              out_tangents, out_tangents_dims, out_dims, batch_size
          )
          return result
  
      def backward(ctx, *grad_outputs):
          assert out_dims != init_val
          assert input_shapes != init_val
          assert saved_tensors_bdims != init_val
  
          def backward_no_context(inputs):
              saved_tensors, grad_outputs = inputs
              wrapped_ctx = CtxWithSavedTensors(ctx, saved_tensors)
              return autograd_function.backward(
                  wrapped_ctx, *grad_outputs
              )
  
          grad_ins, grad_ins_dims = restore_vmap(
              backward_no_context,
              ((saved_tensors_bdims, out_dims),),
              batch_size,
              randomness,
          )((ctx.saved_tensors, grad_outputs))
          result = reductify(
              grad_ins, grad_ins_dims, in_dims, batch_size, input_shapes
          )
          return result
  
      name = f"Vmapped{autograd_function.__name__}"
      Generated = type(
          name,
          (torch.autograd.Function,),
          {
              "forward": staticmethod(forward),
              "backward": staticmethod(backward),
              "jvp": staticmethod(jvp),
              "setup_context": staticmethod(setup_context),
              "generate_vmap_rule": True,
          },
      )
  
      def get_out_dims():
          assert out_dims != init_val
          return out_dims
  
      return Generated, get_out_dims
  
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 9 for calling a global function with example usage: vmapify_autograd_function(autograd_function, in_dims, batch_size, randomness) and returns: Generated,  get_out_dims, outputs, result, result, out_dims, autograd_function, autograd_function