code: def sample_inputs_linalg_solve(
      op_info,
      device,
      dtype,
      requires_grad=False,
      vector_rhs_allowed=True,
      **kwargs,
  ):
      """
      This function generates always solvable input for torch.linalg.solve
      We sample a fullrank square matrix (i.e. invertible) A
      The first input to torch.linalg.solve is generated as the itertools.product of 'batches' and 'ns'.
      The second input is generated as the product of 'batches', 'ns' and 'nrhs'.
      In total this function generates 18 SampleInputs
      'batches' cases include:
          () - single input,
          (0,) - zero batched dimension,
          (2,) - batch of two matrices.
      'ns' gives 0x0 and 5x5 matrices.
      and 'nrhs' controls the number of vectors to solve for:
          () - using 1 as the number of vectors implicitly
          (1,) - same as () but explicit
          (3,) - solve for 3 vectors.
      Zeros in dimensions are edge cases in the implementation and important to test for in order to avoid unexpected crashes.
      'vector_rhs_allowed' controls whether to include nrhs = () to the list of SampleInputs.
      torch.solve / triangular_solve / cholesky_solve (opposed to torch.linalg.solve) do not allow
      1D tensors (vectors) as the right-hand-side.
      Once torch.solve / triangular_solve / cholesky_solve and its testing are removed,
      'vector_rhs_allowed' may be removed here as well.
      """
      make_fullrank = (
          make_fullrank_matrices_with_distinct_singular_values
      )
      make_a = partial(
          make_fullrank,
          dtype=dtype,
          device=device,
          requires_grad=requires_grad,
      )
      make_b = partial(
          make_tensor,
          dtype=dtype,
          device=device,
          requires_grad=requires_grad,
      )
  
      batches = [(), (0,), (2,)]
      ns = [5, 0]
      if vector_rhs_allowed:
          nrhs = [(), (1,), (3,)]
      else:
          nrhs = [(1,), (3,)]
  
      for n, batch, rhs in product(ns, batches, nrhs):
          yield SampleInput(
              make_a(*batch, n, n), args=(make_b(batch + (n,) + rhs),)
          )
  
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 33 for calling a global function with example usage: sample_inputs_linalg_solve(op_info, device, dtype, requires_grad, vector_rhs_allowed) without return types