code: def sample_inputs_i0_i1(
      op_info, device, dtype, requires_grad, **kwargs
  ):
      exclude_zero = requires_grad and op_info.op == torch.special.i0e
      make_arg = partial(
          make_tensor,
          dtype=dtype,
          device=device,
          requires_grad=requires_grad,
          exclude_zero=exclude_zero,
      )
      yield SampleInput(make_arg((S,)))
      yield SampleInput(make_arg(()))
  
      if requires_grad and not exclude_zero:
          # Special Case for gradient
          # Sample with `0` in the input
          t = make_arg((S,))
          t[0] = 0
  
          yield SampleInput(t)
  
file_path: pytorch/torch/testing/_internal/opinfo/definitions/special.py
imports: import unittest
  from functools import partial
  from itertools import product
  from typing import List
  import numpy
  import torch
  from torch.testing import make_tensor
  from torch.testing._internal.common_device_type import precisionOverride, tol, toleranceOverride
  from torch.testing._internal.common_dtype import all_types_and, floating_types
  from torch.testing._internal.common_utils import TEST_SCIPY, torch_to_numpy_dtype_dict
  from torch.testing._internal.opinfo.core import BinaryUfuncInfo, DecorateInfo, L, NumericsFilter, OpInfo, S, SampleInput, UnaryUfuncInfo
  from torch.testing._internal.opinfo.refs import ElementwiseBinaryPythonRefInfo, ElementwiseUnaryPythonRefInfo
  from torch.testing._internal.opinfo.utils import np_unary_ufunc_integer_promotion_wrapper
label_desc: Use image node_id 1 for calling a global function with example usage: sample_inputs_i0_i1(op_info, device, dtype, requires_grad) without return types