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);
}

*   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.

    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));
    }
  }

  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());
      if (!status.ok()) out_tensor->reset();
    }
  }
  if (!status.ok()) {

fi
if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.

  TF_AddInput(desc, r);
  return TF_FinishOperation(desc, s);
}

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s) {
  TF_OperationDescription* desc =
      TF_NewOperation(graph, "RandomUniform", "random_uniform");
  TF_AddInput(desc, {shape, 0});
  TF_SetAttrType(desc, "dtype", dtype);
  return TF_FinishOperation(desc, s);
}

        // Add some trees
        SmbTreeImpl tree1 = session.getSmbTree("share1", null);
        SmbTreeImpl tree2 = session.getSmbTree("share2", null);
        assertNotNull(tree1);
        assertNotNull(tree2);

        // When - Release session to trigger cleanup
        session.release();

{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## Verify the `username` and data shape { #verify-the-username-and-data-shape }

We verify that we get a `username`, and extract the scopes.

   * |      |
   * e ---- c ---- f
   * }
   */
  private static final SuccessorsFunction<Character> JAVADOC_GRAPH =
      createUndirectedGraph("ba", "ad", "be", "ac", "ec", "cf");

  /**
   * A diamond shaped directed graph (arrows going down):
   *
   * {@snippet :
   *   a
   *  / \
   * b   c
   *  \ /
   *   d
   * }
   */
  private static final SuccessorsFunction<Character> DIAMOND_GRAPH =