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();
      }

* The cache has a builtin test, enabled with `UNSAFE_PILOT_ENABLE_RUNTIME_ASSERTIONS=true`, that runs in CI. This will panic if any key is written to with a different value.

#### Partial Computations

Along with caching, partial computations are a critical performance optimization to ensure that we do not need to build (or send) every resource to every proxy on every change. This is discussed more in the Config Serving section.

// Copyright 2020 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// This file implements helper functions for scope position computations.

package syntax

// StartPos returns the start position of n.
func StartPos(n Node) Pos {
	// Cases for nodes which don't need a correction are commented out.
	for m := n; ; {
		switch n := m.(type) {
		case nil:

    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,

// 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">

#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;
};