code: def sample_inputs_linalg_lu(
      op_info, device, dtype, requires_grad=False, **kwargs
  ):
      full_rank = op_info.name == "linalg.lu_factor"
      make_fn = (
          make_tensor
          if not full_rank
          else make_fullrank_matrices_with_distinct_singular_values
      )
      make_arg = partial(
          make_fn,
          dtype=dtype,
          device=device,
          requires_grad=requires_grad,
      )
  
      def out_fn(output):
          if op_info.name == "linalg.lu":
              return output[1], output[2]
          else:
              return output
  
      batch_shapes = ((), (3,), (3, 3))
      # pivot=False only supported in CUDA
      pivots = (
          (True, False)
          if torch.device(device).type == "cuda"
          else (True,)
      )
      deltas = (-2, -1, 0, +1, +2)
      for batch_shape, pivot, delta in product(
          batch_shapes, pivots, deltas
      ):
          shape = batch_shape + (S + delta, S)
          # Insanely annoying that make_fullrank_blablabla accepts a *shape and not a tuple!
          A = make_arg(shape) if not full_rank else make_arg(*shape)
          yield SampleInput(
              A, kwargs={"pivot": pivot}, output_process_fn_grad=out_fn
          )
  
imports: import itertools
  import random
  import unittest
  from functools import partial
  from itertools import chain, product
  from typing import Iterable, List, Tuple
  import numpy
  from numpy import inf
  import torch
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import _get_magma_version, _get_torch_cuda_version, with_tf32_off
  from torch.testing._internal.common_device_type import has_cusolver, skipCPUIfNoLapack, skipCUDAIf, skipCUDAIfNoCusolver, skipCUDAIfNoMagma, skipCUDAIfNoMagmaAndNoCusolver, skipCUDAIfNoMagmaAndNoLinalgsolver, skipCUDAIfRocm, tol, toleranceOverride
  from torch.testing._internal.common_dtype import all_types_and_complex, all_types_and_complex_and, floating_and_complex_types, floating_and_complex_types_and, get_all_complex_dtypes
  from torch.testing._internal.common_utils import GRADCHECK_NONDET_TOL, IS_MACOS, make_fullrank_matrices_with_distinct_singular_values, skipIfSlowGradcheckEnv, slowTest, TEST_WITH_ROCM
  from torch.testing._internal.opinfo.core import clone_sample, DecorateInfo, ErrorInput, gradcheck_wrapper_hermitian_input, L, M, OpInfo, ReductionOpInfo, S, SampleInput
  from torch.testing._internal.opinfo.refs import PythonRefInfo, ReductionPythonRefInfo
label_desc: Use image node_id 36 for calling a global function with example usage: sample_inputs_linalg_lu(op_info, device, dtype, requires_grad) and returns: output,  output, output