resources []config.ResourceSpec
		shapes    []config.UtilizationShapePoint
		wantErrs  field.ErrorList
	}{
		{
			name:   "no shapes",
			shapes: nil,
			wantErrs: field.ErrorList{
				{
					Type:  field.ErrorTypeRequired,
					Field: "scoringStrategy.shape",
				},
			},
		},
		{
			name:   "weight greater than max",
			shapes: defaultShape,
			resources: []config.ResourceSpec{

namespace tensorflow {

class XlaDevice : public LocalDevice {
 public:
  // Given a tensor, sets `xla::Shape*` the shape of tensor's representation
  // on device, fully padded. On error, the contents of `xla::Shape*`
  // are undefined.
  typedef std::function<Status(const Tensor&, xla::Shape*)> PaddedShapeFn;

  // Wrapper class to store metadata about the XlaDevice, where it can be

  // Check XlaHostCompute nodes' "shapes" attr. "0" should not have shapes, and
  // "1" should have shapes.
  std::vector<TensorShapeProto> shapes;
  TF_CHECK_OK(GetNodeAttr(host_compute_0->attrs(), "shapes", &shapes));
  EXPECT_EQ(shapes.size(), 0);
  TF_CHECK_OK(GetNodeAttr(host_compute_1->attrs(), "shapes", &shapes));
  EXPECT_EQ(shapes.size(), 1);
  EXPECT_EQ(shapes[0].dim_size(), 1);

    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(ArraySlice<int64_t>(
            reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
      }
    }
    op_->node_builder.Attr(attr_name, shapes);
    return absl::OkStatus();
  }

void SetShapeAttribute(absl::string_view name, ContainerT shapes,
                       AttrValueMap* values) {
  AttrValue value;
  auto& shape_list = *value.mutable_list();
  for (const std::optional<llvm::ArrayRef<int64_t>>& shape : shapes) {
    TensorShapeProto& tshape = *shape_list.add_shape();
    if (shape.has_value()) {
      for (int64_t dim : *shape) {

      new ParallelTensor(parallel_device, std::move(components), dtype));
}

Status ParallelTensor::Shape(const std::vector<int64_t>** shape) const {
  if (!shape_.has_value()) {
    TF_Status status;
    PartialTensorShape combined_shape;
    TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&combined_shape));

    for (const TensorHandlePtr& component : tensors_) {
      PartialTensorShape component_shape;

  DimensionVector contracting_dimensions_;
  // Out dimensions are any dimensions that are neither batch nor contracting
  // dimensions, hence will be propagated to output shape.
  DimensionVector out_dimensions_;
};

// Calculates the flattened shapes for dynamic shaped operands in
// mhlo.dot_general:
//   1. flattened_out_dim = UnsortedSegmentProdOp(operand_shape, out_axes)

  return num_contract_dim;
}

// Create a matrix with 1s using the given shape.
Operation *Create1sMatrix(OpBuilder &builder, Location loc,
                          const SmallVector<int64_t> &shape) {
  SmallVector<int64_t> shape_ones(/*Size=*/shape.size(), /*Value=*/1);

  return builder.create<TF::BroadcastToOp>(
      loc, RankedTensorType::get(shape, builder.getIntegerType(32)),

  // CHECK-DAG:  %[[CSTR_LHS_SHAPE:.+]] = shape.shape_of %arg0
  // CHECK-DAG:  %[[CSTR_RHS_SHAPE:.+]] = shape.shape_of %arg1
  // CHECK-NEXT: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[CSTR_LHS_SHAPE]], %[[CSTR_RHS_SHAPE]]
  // CHECK-NEXT: shape.assuming %[[WITNESS:.+]]
  // CHECK-DAG:    %[[LHS_SHAPE:.+]] = shape.shape_of %arg0
  // CHECK-DAG:    %[[RHS_SHAPE:.+]] = shape.shape_of %arg1

  TF_RETURN_IF_ERROR(ParseNodeShapes(shapes, node_shapes));
  return ParseInputArrayInfo(node_names, node_dtypes, node_shapes, inputs);
}

static absl::StatusOr<std::vector<int>> ParseShapeStr(
    absl::string_view node_shapes_str) {
  std::vector<int> dims;
  for (absl::string_view dim_str : absl::StrSplit(node_shapes_str, ',')) {
    // Treats empty input shape as scalar
    if (dim_str.empty()) continue;