code: def sample_inputs_conversion(
      op_info, device, dtype, requires_grad, **kwargs
  ):
      make_arg = partial(
          make_tensor,
          dtype=dtype,
          device=device,
          requires_grad=requires_grad,
      )
      shapes = ((), (2, 3))
      memory_format_options = [None, torch.contiguous_format]
      for shape, memory_format in itertools.product(
          shapes, memory_format_options
      ):
          yield SampleInput(
              make_arg(shape),
              kwargs={"memory_format": memory_format}
              if memory_format
              else {},
          )
  
file_path: pytorch/test/functorch/functorch_additional_op_db.py
imports: from functools import partial
  import itertools
  import unittest
  import torch
  from torch.testing._internal.common_dtype import floating_types, floating_types_and, all_types_and_complex_and
  from torch.testing._internal.common_utils import make_tensor
  from torch.testing._internal.common_methods_invocations import OpInfo, SampleInput, DecorateInfo
label_desc: Use image node_id 9 for calling a global function with example usage: sample_inputs_conversion(op_info, device, dtype, requires_grad) without return types