TEST_F(CApiAttributesTest, ShapeList) {
  const int64_t shape_1[] = {1, 3};
  const int64_t shape_2[] = {2, 4, 6};
  const int64_t* list[] = {&shape_1[0], &shape_2[0]};
  const size_t list_size = TF_ARRAYSIZE(list);
  const int ndims[] = {TF_ARRAYSIZE(shape_1), TF_ARRAYSIZE(shape_2)};
  const int total_ndims = 5;  // ndims[0] + ndims[1]

  auto desc = init("list(shape)");
  TF_SetAttrShapeList(desc, "v", list, ndims, list_size);

  std::vector<PartialTensorShape> shapes;
  shapes.reserve(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (num_dims[i] < 0) {
      shapes.emplace_back();
    } else {
      shapes.emplace_back(absl::Span<const int64_t>(
          reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
    }
  }
  desc->node_builder.Attr(attr_name, shapes);
}

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//

// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * An invalid shape is being set (e.g., the shape being set
//     is incompatible with the existing shape).
TF_CAPI_EXPORT extern void TF_GraphSetTensorShape(TF_Graph* graph,

    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.

        populatedArray.count = 3;
        populatedArray.s = new netdfs.DfsInfo200[3];

        // Create DFS root entries
        String[] rootNames = { "share1", "share2", "share3" };
        for (int i = 0; i < 3; i++) {
            netdfs.DfsInfo200 entry = new netdfs.DfsInfo200();
            entry.dfs_name = rootNames[i];
            populatedArray.s[i] = entry;
        }

        // Create real ShareInfo1 objects instead of mocks
        srvsvc.ShareInfo1 shareInfo1 = new srvsvc.ShareInfo1();
        shareInfo1.netname = "Share1";
        shareInfo1.type = 0;
        shareInfo1.remark = "Remark for Share1";

        srvsvc.ShareInfo1 shareInfo2 = new srvsvc.ShareInfo1();
        shareInfo2.netname = "Share2";
        shareInfo2.type = 1;

*   Tracing/timeline support for distributed runtime (no GPU profiler yet).
*   C API gives access to inferred shapes with `TF_GraphGetTensorNumDims` and
    `TF_GraphGetTensorShape`.
*   Shape functions for core ops have moved to C++ via
    `REGISTER_OP(...).SetShapeFn(...)`. Python shape inference RegisterShape
    calls use the C++ shape functions with `common_shapes.call_cpp_shape_fn`. A
    future release will remove `RegisterShape` from python.

        assertEquals(2, response.results.length, "Should have 2 share entries.");

        SmbShareInfo share1 = (SmbShareInfo) response.results[0];
        assertEquals("SHARE1", share1.getName().trim(), "Share 1 name should be correct.");
        assertEquals(0, share1.type, "Share 1 raw type should be 0.");
        assertEquals("Remark 1", share1.remark.trim(), "Share 1 remark should be correct.");

    }

    /**
     * Test of equals method, of class SmbShareInfo.
     */
    @Test
    void testEquals() {
        SmbShareInfo instance1 = new SmbShareInfo("SHARE1", 0, "remark1");
        SmbShareInfo instance2 = new SmbShareInfo("SHARE1", 1, "remark2");
        SmbShareInfo instance3 = new SmbShareInfo("SHARE2", 0, "remark1");
        Object notAShareInfo = new Object();