// a) argument and return value, for entry computations b) variables, for
    // all computations, should be represented in XLA. Parameters/return values
    // will be shaped according to the function pair, and reshaped back to/from
    // their declared shapes for computations. Must be non-empty.
    std::vector<XlaShapeLayoutHelpers::ShapeDeterminationFns>
        shape_determination_fns;

// Rewrites computations generated by the xla.compile() Python code into
// XlaLaunch nodes.
//
// xla.compile() does two main things:
// a) marks operators that make up an XLA computation with the attribute
//    _xla_compile_id=XYZ, where XYZ is a unique key.
// b) adds XlaClusterOutput nodes to represent outputs of the computation.
//    These nodes are not marked with the _xla_compile_id attribute.

    // a) argument and return value, for entry computations b) variables, for
    // all computations, should be represented in XLA. Parameters/return values
    // will be shaped according to the function pair, and reshaped back to/from
    // their declared shapes for computations. Must be non-empty.
    std::vector<XlaShapeLayoutHelpers::ShapeDeterminationFns>

import java.util.function.Supplier

class ManualEvictionInMemoryCacheTest extends Specification {

    @Timeout(value = 5, unit = TimeUnit.SECONDS)
    def "supports #concurrency concurrent computations"() {
        def latch = new CountDownLatch(concurrency)
        def executor = Executors.newFixedThreadPool(concurrency)
        def cache = new ManualEvictionInMemoryCache<String, String>()

        when:

      return errors::InvalidArgument(
          "Undeclared output of XLA computation. Some common causes of this "
          "error are: 1) variable initializers that depend on the XLA "
          "computation; 2) gradient computations that depend on the XLA "
          "computation, which can be mitigated by moving gradient computations "
          "inside XLA computation. Offending edge: ",

#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"

namespace mlir {
namespace quant {

// Check if the op has data movement trait. Ops with this trait do not perform
// any computations but just move data and has one result operand.
bool IsOpWithDataMovementTrait(Operation* op);

// Check if the op is quantizable. Currently, the scope of quantizable op is

// a customizable variant of SHAKE128.
// N is used to define functions based on cSHAKE, it can be empty when plain cSHAKE is
// desired. S is a customization byte string used for domain separation - two cSHAKE
// computations on same input with different S yield unrelated outputs.
// When N and S are both empty, this is equivalent to NewShake128.
func NewCShake128(N, S []byte) ShakeHash {
	if len(N) == 0 && len(S) == 0 {
		return NewShake128()

limitations under the License.
==============================================================================*/
//
// This file defines support utilities for interoperating with FakeQuant* based
// QAT (Quantized Aware Training) computations, as implemented by TFLite. Note
// that FakeQuant* operators mix multiple concerns specific to how TFLite
// originally implemented quantization. As such, utilities here enforce

          compilation_result->computation->proto(), xla::DebugOptions()));

  TF_ASSIGN_OR_RETURN(
      std::unique_ptr<xla::HloModule> hlo_module,
      xla::HloModule::CreateFromProto(compilation_result->computation->proto(),
                                      hlo_module_config));

  std::string all_computations;
  for (auto computation : hlo_module->computations()) {

}

def TfDevice_ReplicateOp : TfDevice_Op<"replicate",
    [SingleBlockImplicitTerminator<"ReturnOp">, AttrSizedOperandSegments]> {
  let summary = "Wraps an N-way replicated computation.";

  let description = [{
The region held by this operation represents a computation that is replicated
across multiple devices. The number of replications is based on the `n`
attribute. Explicit devices can be populated in the `devices` attribute, and it