code: def sample_inputs_lu_solve(
      op_info, device, dtype, requires_grad=False, **kwargs
  ):
      """Samples the inputs for both linalg.lu_solve and lu_solve"""
      make_fn = make_fullrank_matrices_with_distinct_singular_values
      make_a = partial(make_fn, dtype=dtype, device=device)
      make_b = partial(make_tensor, dtype=dtype, device=device)
  
      def clone(X, requires_grad):
          Y = X.clone()
          Y.requires_grad_(requires_grad)
          return Y
  
      is_linalg_lu_solve = op_info.name == "linalg.lu_solve"
  
      batches = ((), (0,), (2,))
      ns = (3, 1, 0)
      nrhs = (4, 1, 0)
  
      for n, batch, rhs in product(ns, batches, nrhs):
          A = make_a(*(batch + (n, n)))
          LU, pivots = torch.linalg.lu_factor(A)
  
          B = make_b(batch + (n, rhs))
  
          grads = (False,) if not requires_grad else (True, False)
          # we try all possible combinations of requires_grad for each input
          for LU_grad, B_grad in product(grads, grads):
              # when requires_grad == True, at least one input has to have requires_grad enabled
              if requires_grad and not LU_grad and not B_grad:
                  continue
  
              if is_linalg_lu_solve:
                  for adjoint, left in product((True, False), repeat=2):
                      yield SampleInput(
                          clone(LU, LU_grad),
                          args=(
                              pivots,
                              clone(B if left else B.mT, B_grad),
                          ),
                          kwargs=dict(adjoint=adjoint, left=left),
                      )
              else:
                  yield SampleInput(
                      clone(B, B_grad),
                      args=(clone(LU, LU_grad), pivots),
                  )
  
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 9 for calling a global function with example usage: sample_inputs_lu_solve(op_info, device, dtype, requires_grad) and returns: Y