// suggest a representation, namely using 1-1-1 00:00:00 UTC as the
// epoch, and that's what we do.
//
// The Add and Sub computations are oblivious to the choice of epoch.
//
// The presentation computations - year, month, minute, and so on - all
// rely heavily on division and modulus by positive constants. For
// calendrical calculations we want these divisions to round down, even

      location, output_type, concat_dimension_op.getOutput(), inputs);
}

// For tile sharded inputs to TPU computation, inject split op between the
// input values and TPU computation so that tiled input values are passed in
// as inputs to TPU computations. If more than one dimension is sharded, then
// a tree of connected split ops are added before tf_device.parallel_execute op.

  let summary = "Decompose composite resource variable operations into primitive Read/AssignVariableOp and raw computation.";

  let description = [{
    A pass that decomposes composite resource operations into primitive ones like
    ReadVariableOp, AssignVariableOp and other computations to facilitate
    transformations like resource op lifting.

    For example:

    ```mlir

// Creates a pass that lifts operations on external resource variables from
// device computation nested in `tf_device::LaunchOp` out so that resource
// variable load operations are all before device computation while resource
// variable store operations are all after device computation. After this pass,
// device computation no longer interacts with external resource variables.

Those worker processes would be the ones running your application, they would perform the main computations to receive a **request** and return a **response**, and they would load anything you put in variables in RAM.

<img src="/img/deployment/concepts/process-ram.svg">

    int numActions = 100;
    // hashcodes from non-overlapping hash computations
    HashCode expected1 = randomHash(hashFunction, new Random(1L), numActions);
    HashCode expected2 = randomHash(hashFunction, new Random(2L), numActions);

    // equivalent, but overlapping, computations (should produce the same results as above)
    Random random1 = new Random(1L);
    Random random2 = new Random(2L);

#define GEN_PASS_DEF_FUSEDKERNELMATCHERPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

// Optimizes TF computations by fusing subgraphs/nodes onto more efficient
// implementations to decrease the number of operations needed to perform a
// computation.
struct FusedKernelMatcherPass
    : public impl::FusedKernelMatcherPassBase<FusedKernelMatcherPass> {
  void runOnOperation() override;
};

      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>