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()) {

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()),

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all

proxy1     cluster1     STALE        SYNCED      SYNCED      NOT SENT     SYNCED       istiod1     1.20
proxy2     cluster2     STALE        SYNCED      STALE       SYNCED       STALE        istiod2     1.19
proxy3     cluster3     NOT SENT     ERROR       STALE       NOT SENT     NOT SENT     istiod3     1.20

    sequence_shapes = {
        SHAPE_LIST,
        SHAPE_SET,
        SHAPE_FROZENSET,
        SHAPE_TUPLE,
        SHAPE_SEQUENCE,
        SHAPE_TUPLE_ELLIPSIS,
    }
    sequence_shape_to_type = {
        SHAPE_LIST: list,
        SHAPE_SET: set,
        SHAPE_TUPLE: tuple,
        SHAPE_SEQUENCE: list,
        SHAPE_TUPLE_ELLIPSIS: list,
    }

    @dataclass

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2beta2";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to

						clusterID:      "cluster2",
						version:        "1.19",
						cdsSyncStatus:  status.ConfigStatus_STALE,
						ldsSyncStatus:  status.ConfigStatus_SYNCED,
						rdsSyncStatus:  status.ConfigStatus_SYNCED,
						edsSyncStatus:  status.ConfigStatus_STALE,
						ecdsSyncStatus: status.ConfigStatus_STALE,
					},
				}),
				"istiod3": xdsResponseInput("istiod3", []clientConfigInput{
					{
						proxyID:        "proxy3",

      if (combined_shape.dims() < 0 ||
          combined_shape.dims() != component_shape.dims()) {
        PartialTensorShape first_shape;
        TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&first_shape));
        return errors::Unimplemented(absl::StrCat(
            "Computing the shape of a ParallelTensor when the components do "
            "not all have the same rank is not supported. One tensor had "
            "shape ",

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2beta1";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to