code: def custom_function_call_vmap_generate_rule(
      interpreter, autograd_function, *operands
  ):
      unwrapped_operands, in_dims = unwrap_batched(
          operands, interpreter.level()
      )
      vmapped_function, get_out_dims = vmapify_autograd_function(
          autograd_function,
          in_dims,
          interpreter.batch_size(),
          interpreter.randomness(),
      )
  
      with interpreter.lower():
          output = custom_function_call(
              vmapped_function, *unwrapped_operands
          )
  
      out_dims = get_out_dims()
      return wrap_batched(output, out_dims, interpreter.level())
  
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 7 for calling a global function with example usage: custom_function_call_vmap_generate_rule(interpreter, autograd_function) and returns: wrap_batched