code: def sample_inputs_linalg_ldl_solve(
      op_info, device, dtype, requires_grad=False, **kwargs
  ):
      # Generate LDL factors of symmetric (and Hermitian on CPU) matrices
      from torch.testing._internal.common_utils import (
          random_hermitian_pd_matrix,
          random_symmetric_pd_matrix,
      )
  
      device = torch.device(device)
      symmetric_inputs = (
          random_symmetric_pd_matrix(
              S, dtype=dtype, device=device
          ),  # single matrix
          random_symmetric_pd_matrix(
              S, 2, dtype=dtype, device=device
          ),  # batch of matrices
          torch.zeros(0, 0, dtype=dtype, device=device),  # 0x0 matrix
          torch.zeros(
              0, 2, 2, dtype=dtype, device=device
          ),  # zero batch of matrices
      )
      hermitian_inputs = (
          (
              random_hermitian_pd_matrix(S, dtype=dtype, device=device),
              random_hermitian_pd_matrix(
                  S, 2, dtype=dtype, device=device
              ),
          )
          if device.type == "cpu" and dtype.is_complex
          else ()
      )
      test_cases1 = (
          torch.linalg.ldl_factor_ex(a, hermitian=False)
          for a in symmetric_inputs
      )
      test_cases2 = (
          torch.linalg.ldl_factor_ex(a, hermitian=True)
          for a in hermitian_inputs
      )
  
      # Symmetric case
      make_arg = partial(
          make_tensor,
          device=device,
          dtype=dtype,
          requires_grad=requires_grad,
      )
      for test_case in test_cases1:
          factors, pivots, _ = test_case
          factors.requires_grad = requires_grad
          for B_batch_shape in ((), factors.shape[:-2]):
              B = make_arg((*B_batch_shape, factors.shape[-1], S))
              yield SampleInput(
                  factors,
                  args=(pivots, B),
                  kwargs=dict(hermitian=False),
              )
              clone_factors = (
                  factors.detach().clone().requires_grad_(requires_grad)
              )
              yield SampleInput(
                  clone_factors,
                  args=(pivots, B),
                  kwargs=dict(hermitian=False),
              )
  
      # Hermitian case
      for test_case in test_cases2:
          factors, pivots, _ = test_case
          factors.requires_grad = requires_grad
          for B_batch_shape in ((), factors.shape[:-2]):
              B = make_arg((*B_batch_shape, factors.shape[-1], S))
              yield SampleInput(
                  factors, args=(pivots, B), kwargs=dict(hermitian=True)
              )
              clone_factors = (
                  factors.detach().clone().requires_grad_(requires_grad)
              )
              yield SampleInput(
                  clone_factors,
                  args=(pivots, B),
                  kwargs=dict(hermitian=True),
              )
  
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 22 for calling a global function with example usage: sample_inputs_linalg_ldl_solve(op_info, device, dtype, requires_grad) without return types