xla::XlaComputation computation,
      return_value.valid() ? builder.Build(return_value) : builder.Build());
  auto hlo_module = computation.proto();
  xla::HloProto hlo_proto;
  hlo_proto.mutable_hlo_module()->Swap(&hlo_module);

  compilation_result->computation = std::make_shared<xla::XlaComputation>();
  xla::XlaComputation* xla_computation = compilation_result->computation.get();

	logger := klog.FromContext(ctx)
	// If a pod doesn't have any volume attached to it, the predicate will always be true.
	// Thus we make a fast path for it, to avoid unnecessary computations in this case.
	if len(pod.Spec.Volumes) == 0 {
		return nil
	}
	var podPVTopologies []pvTopology
	state, err := getStateData(cs)
	if err != nil {
		// Fallback to calculate pv list here

// function/graph/cluster into an XlaCompilationResult (HLO) and
// `ExecutableType` and tries saving/persisting the compiled HLO and executable
// to disk.
//
// Since XLA computations must have static shapes, DeviceCompiler generates a
// new XLA computation for each new set of input shapes.
// TODO(b/255826209): De-templatize once we've moved to Device API completely.
template <typename ExecutableType, typename ClientType>

 * </p>
 *
 * <p>
 * A typical use of a provider is to pass values from one Gradle model element to another, e.g. from a project extension
 * to a task, or between tasks. Providers also allow expensive computations to be deferred until their value is actually
 * needed, usually at task execution time.
 * </p>
 *
 * <p>
 * There are a number of ways to create a {@link Provider} instance. Some common methods:
 * </p>
 *

    XlaComputation& computation) {
  xla::DebugOptions debug_options;
  TF_ASSIGN_OR_RETURN(auto hlo_module_config,
                      xla::HloModule::CreateModuleConfigFromProto(
                          computation.proto(), debug_options));
  TF_ASSIGN_OR_RETURN(
      std::unique_ptr<xla::HloModule> hlo_module,
      xla::HloModule::CreateFromProto(computation.proto(), hlo_module_config));

    replicated TPU computation. The number of times a TPU computation is
    replicated is defined in the `tf.TPUReplicateMetadata` op (`num_replicas`
    attribute) and operand and result sizes of `tf.TPUReplicatedInput` and
    `tf.TPUReplicatedOutput` respectively must match, excluding packed tensors.
    It is also assumed ops of the same TPU computation do not have ops outside

			c = High
		}
	}

	// We store the results of the computations of the tie-breaker rules along
	// with the best match. There is no need to do the checks once we determine
	// we have a winner, but we do still need to do the tie-breaker computations.
	// We use "beaten" to keep track if we still need to do the checks.

#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/util/command_line_flags.h"

namespace tensorflow {

struct XlaAutoJitFlag {
  // Control compilation of operators into XLA computations on CPU and GPU
  // devices.  0 = use ConfigProto setting; -1 = off; 1 = on for things very
  // likely to be improved; 2 = on for everything.
  //

		if err != nil {
			t.Fatal(err)
		}
		aliceSecret, err := aliceKey.ECDH(bobKey.PublicKey())
		if err != nil {
			t.Fatal(err)
		}

		if !bytes.Equal(bobSecret, aliceSecret) {
			t.Error("two ECDH computations came out different")
		}
	})
}

type countingReader struct {
	r io.Reader
	n int
}

func (r *countingReader) Read(p []byte) (int, error) {
	n, err := r.r.Read(p)
	r.n += n
	return n, err

void AppendMarkForCompilationPassFlagsInternal(std::vector<Flag>* flag_list) {
  std::vector<Flag> new_flags = {
      Flag("tf_xla_auto_jit", SetterForXlaAutoJitFlag, "0",
           "Control compilation of operators into XLA computations on CPU and "
           "GPU devices.  0 = use ConfigProto setting; -1 = off; 1 = on for "
           "things very likely to be improved; 2 = on for everything; "