Mode:       0664,
			Uid:        1000,
			Gid:        1000,
			Uname:      "shane",
			Gname:      "shane",
			Size:       7,
			ModTime:    time.Unix(1350244992, 23960108),
			ChangeTime: time.Unix(1350244992, 23960108),
			AccessTime: time.Unix(1350244992, 23960108),
			Typeflag:   TypeReg,

    link: https://medium.com/@arthur393/another-boilerplate-to-fastapi-azure-pipeline-ci-pytest-3c8d9a4be0bb
    title: 'Another Boilerplate to FastAPI: Azure Pipeline CI + Pytest'
  - author: Shane Soh
    author_link: https://medium.com/@shane.soh
    link: https://medium.com/analytics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece
    title: Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker

			testHeader{Header{
				Typeflag: TypeReg,
				Name:     strings.Repeat("longname/", 15) + "file.txt",
				Size:     6,
				Mode:     0644,
				Uid:      501,
				Gid:      20,
				Uname:    "shane",
				Gname:    "staff",
				ModTime:  time.Unix(1360135598, 0),
			}, nil},
			testWrite{"hello\n", 6, nil},
			testClose{nil},
		},
	}, {
		// This file was produced using GNU tar v1.26:

        `max_batch_size` for building TensorRT engines. Add parameters
        `use_dynamic_shape` to enable dynamic shape support. The default is to
        disable dynamic shape support. Add `dynamic_shape_profile_strategy` for
        selecting a dynamic shape profile strategy. The default is profile
        strategy is `Range`.
    *   Issue a warning when function get_tensorrt_rewriter_config is used.

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {
    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),

    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);
    if (shape.num_dims == InferenceContext::kUnknownRank) {
      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));

                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {

     */
    int connectionState;
    int tid;

    String share;
    String service = "?????";
    String service0;
    SmbSession session;
    boolean inDfs, inDomainDfs;
    int tree_num; // used by SmbFile.isOpen

    SmbTree( SmbSession session, String share, String service ) {
        this.session = session;
        this.share = share.toUpperCase();
        if( service != null && service.startsWith( "??" ) == false ) {

  auto c_api_client = down_cast<xla::PjRtCApiClient*>(pjrt_client->get());
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});

  auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer(
      data.data(), shape.element_type(), shape.dimensions(),
      /*byte_strides=*/std::nullopt,
      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,

    /**
     * 
     */
    private TestProperties () {}

    static final String TEST_SHARE_URL_MAIN = "test.share.main.url";
    static final String TEST_SHARE_URL_DFSROOT = "test.share.dfsroot.url";

    static final String TEST_SHARE_MAIN = "test.share.main";
    static final String TEST_SHARE_GUEST = "test.share.guest";

    static final String TEST_USER_NAME = "test.user.name";