const ShapedType &padding_ty = padding_attr.getType();
  if (padding_ty.getRank() != 2 || padding_ty.getDimSize(1) != 2)
    return op.emitOpError()
           << "expects padding to be a matrix with minor dimension 2";

  if (lhs_dilation_attr.getType().getRank() != 1)
    return op.emitOpError() << "expects lhs_dilation to be a vecotr";

logo-teal-vector.svg...

// license that can be found in the LICENSE file.

package s390x

import (
	"cmd/internal/obj"
)

// This file contains utility functions for use when
// assembling vector instructions.

// vop returns the opcode, element size and condition
// setting for the given (possibly extended) mnemonic.
func vop(as obj.As) (opcode, es, cs uint32) {
	switch as {
	default:
		return 0, 0, 0

logo-teal-vector.svg...

  %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  // expected-error@+1 {{'tf.XlaConvV2' op expects lhs_dilation to be a vecotr}}

	vectorsJSON, err := os.ReadFile("testdata/rfc9180-vectors.json")
	if err != nil {
		t.Fatal(err)
	}

	var vectors []struct {
		Name        string
		Setup       string
		Encryptions string
	}
	if err := json.Unmarshal(vectorsJSON, &vectors); err != nil {
		t.Fatal(err)
	}

	for _, vector := range vectors {
		t.Run(vector.Name, func(t *testing.T) {
			setup := parseVectorSetup(vector.Setup)

Roland Shoemaker <******@****.***> 1715710616 -0700

func TestEd25519Vectors(t *testing.T) {
	jsonVectors := downloadEd25519Vectors(t)
	var vectors []struct {
		A, R, S, M string
		Flags      []string
	}
	if err := json.Unmarshal(jsonVectors, &vectors); err != nil {
		t.Fatal(err)
	}
	for i, v := range vectors {
		expectedToVerify := true
		for _, f := range v.Flags {
			switch f {

  // Given the required_consts, it will fill the 3 output vectors with
  // their respective data.
  // Expressions: Output XLA expressions as required by the compiled kernel.
  // Tensors: Vector of tensors that back the TensorValue inputs
  // Inputs: Vector of inputs that are backed by tensors.
  mlir::LogicalResult PrepareKernelInputs(
      const llvm::SmallDenseSet<int>& required_consts,
      std::vector<tensorflow::XlaExpression>& expressions,

class XlaLocalLaunchBase : public AsyncOpKernel {
 public:
  XlaLocalLaunchBase(OpKernelConstruction* ctx,
                     const std::vector<int>& constants,
                     const std::vector<int>& resources,
                     const NameAttrList& function, bool has_ref_vars);
  XlaLocalLaunchBase(const XlaLocalLaunchBase&) = delete;