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_values);
    for (int i = 0; i < num_values; ++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]));
      }
    }
    op_->node_builder.Attr(attr_name, shapes);
    return absl::OkStatus();
  }

  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.
//

  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual absl::Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.

 public:
  CheckpointReader(const std::string& filename, TF_Status* status);

  bool HasTensor(const std::string& name) const;
  const std::string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a

TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return an identity op.
TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// 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,

        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;
        }

    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.