code: def sample_inputs_masked_fill(
      op_info, device, dtype, requires_grad, **kwargs
  ):
      S = 3
      make_arg = partial(
          make_tensor,
          device=device,
          dtype=dtype,
          requires_grad=requires_grad,
      )
  
      yield SampleInput(
          make_arg((S, S)),
          args=(torch.randn(S, S, device=device) > 0, 10),
      )
      yield SampleInput(
          make_arg((S, S)), args=(torch.randn(S, device=device) > 0, 10)
      )
      yield SampleInput(
          make_arg(()), args=(torch.randn((), device=device) > 0, 10)
      )
      yield SampleInput(
          make_arg((S, S)),
          args=(torch.randn((), device=device) > 0, 10),
      )
      yield SampleInput(
          make_arg((S,)),
          args=(torch.randn(S, S, device=device) > 0, 10),
          broadcasts_input=True,
      )
  
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 7 for calling a global function with example usage: sample_inputs_masked_fill(op_info, device, dtype, requires_grad) without return types