std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config(
      TF_CreateConfig(
          /*enable_xla_compilation=*/false,
          /*gpu_memory_allow_growth=*/true, /*num_cpu_devices=*/2),
      TF_DeleteBuffer);
  TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length,

  /// Return a new scope. All ops created within the returned scope will have no
  /// control dependencies on other operations.
  Scope WithNoControlDependencies() const;

  /// Return a new scope. All ops created within the returned scope will have
  /// the device field set to 'device'.
  Scope WithDevice(const string& device) const;

  /// Returns a new scope.  All ops created within the returned scope will have

			if testCase.expectedError {
				return
			}

			if !reflect.DeepEqual(opts, testCase.expectedValue) {
				t.Fatalf("value mismatch with args %v expected value %v got %v",
					testCase.policyOptions, testCase.expectedValue, opts)
			}
		})
	}
}

func newCPUSetPtr(cpus ...int) *cpuset.CPUSet {
	ret := cpuset.New(cpus...)
	return &ret

type client[ObjectType configurationObject] interface {
	Create(ctx context.Context, obj ObjectType, opts metav1.CreateOptions) (ObjectType, error)
	Update(ctx context.Context, obj ObjectType, opts metav1.UpdateOptions) (ObjectType, error)
	Delete(ctx context.Context, name string, opts metav1.DeleteOptions) error
}

// cache is the needed fragment of the typed generated access ("lister") to an informer's local cache

//   2. Find users not in cluster that are interleaved between cluster ops.
//   3. Find external uses of cluster ops.
//   4. Create `tf_device.cluster` with results consisting of the external uses
//      of cluster ops determined at 3.
//   5. Move cluster ops to `tf_device.cluster` body.
//   6. Replace external uses of cluster ops uses with `tf_device.cluster`
//      results.
//   7. Move users from 2 to after the `tf_device.cluster`.

      !TFL::TFTypeIsBFloat16OrHalfTensor(op.getInput())) {
    return rewriter.notifyMatchFailure(
        op, "op's input is not float or half or bfloat16");
  }
  if (!TFL::TFDataFormatIsNHWC(op)) {
    return rewriter.notifyMatchFailure(op, "op's data format isn't NHWC");
  }

  // Allow dynamic width and height dimensions only.
  auto result_ty = mlir::cast<TensorType>(op.getResult().getType());

namespace {

// Exhaust search in the block to get all the ops that have data dependency of
// the cluster.
llvm::SetVector<Operation*> GetAllOpsDependOnCluster(
    const Cluster& c,
    const llvm::DenseMap<Operation*, Cluster*>& op_to_cluster_map) {
  llvm::SetVector<Operation*> ops_depend_on_cluster;
  for (Operation& op : *c.ops.front()->getBlock()) {
    if (op.isBeforeInBlock(c.ops.front()) || c.ops.contains(&op)) {
      continue;
    }

  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config(
      TF_CreateConfig(
          /*xla*/ false,
          /* gpu_memory_allow_growth */ true, /* num_cpu_devices */
          2),
      TF_DeleteBuffer);
  TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length,

// grouping control dependencies. We don't want to report warnings for these
// ops, unless they belong to a control path between two side-effecting ops that
// should not have any dependencies.
bool IsIntermediateOp(IslandOp island) {
  // These two side-effect-free ops are known to be used for control dependency
  // grouping (e.g., in `BreakUpIslands` pass).

Tres Popp <******@****.***> 1648211118 -0700