code: def make_prim(
      schema: str,
      impl_aten,
      return_type=_prims.RETURN_TYPE.NEW,
      doc: str = "",
      tags: Optional[Sequence[torch.Tag]] = None,
  ):
      def meta(*args, **kwargs):
          return _prims.TensorMeta(impl_aten(*args, **kwargs))
  
      return _prims._make_prim(
          schema=schema,
          return_type=return_type,
          meta=meta,
          impl_aten=impl_aten,
          doc=doc,
          tags=tags,
      )
  
imports: from __future__ import annotations
  import logging
  from typing import Optional, Sequence
  import torch
  from torch import _prims, Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: make_prim(schema, impl_aten, return_type, doc, tags) and returns: _prims, _prims