if (!operand_type.hasStaticShape() || !indices_type.hasStaticShape() ||
          !updates_type.hasStaticShape()) {
        return failure();
      }

      // Match the scatter computation against computations supported by TF.
      if (failed(MatchBinaryReduceFunction<BinaryOp>(
              scatter_op.getUpdateComputation()))) {
        return failure();
      }

    mlir::TF::RuntimeDevices devices, std::string* host_device);

// Parses XLA compilation and execution devices from a tf_device.cluster and
// returns the host device for the head and tail computations. For TPU device,
// if the computation is replicated, GetDeviceAliasForHostOfLogicalCore(0) is
// returned instead.
mlir::LogicalResult GetHostDeviceOutsideComputation(
    mlir::TF::RuntimeDevices devices, mlir::tf_device::ClusterOp cluster,

      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">

#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();

 * </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));