MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(GetOpCostPass)

  llvm::StringRef getArgument() const final { return "tfl-get-op-cost"; }
  llvm::StringRef getDescription() const final {
    return "Get cost for every op";
  }
  void runOnOperation() override;
};

void GetOpCostPass::runOnOperation() {
  auto func = getOperation();
  OpBuilder builder(func);

    auto cost_attr = device_costs_attr.getNamed(kv.first);
    if (!cost_attr.has_value()) return std::nullopt;
    float cost = mlir::dyn_cast_or_null<mlir::FloatAttr>(cost_attr->getValue())
                     .getValueAsDouble();
    device_costs[kv.second] = cost;
  }
  return device_costs;
}

flatbuffers::Offset<SubgraphMetadata> CreateSubgraphMetadata(

	// This is a *client side* filter. This means CPU/memory costs are still present for filtered objects.
	// Use LabelSelector or FieldSelector instead, if possible.
	ObjectFilter DynamicObjectFilter
	// ObjectTransform allows arbitrarily modifying objects stored in the underlying cache.
	// If unset, a default transform is provided to remove ManagedFields (high cost, low value)
	ObjectTransform func(obj any) (any, error)
}

 * could in principle cause problems for some users. Still, we expect that the benefits of the
 * nullness annotations in particular will outweigh the costs. (And it's worth noting that we have
 * released multiple ListenableFuture.class files that are not byte-for-byte compatible even from
 * the beginning, thanks to using different `-source -target` values for compiling our `-jre` and

 * could in principle cause problems for some users. Still, we expect that the benefits of the
 * nullness annotations in particular will outweigh the costs. (And it's worth noting that we have
 * released multiple ListenableFuture.class files that are not byte-for-byte compatible even from
 * the beginning, thanks to using different `-source -target` values for compiling our `-jre` and

 * could in principle cause problems for some users. Still, we expect that the benefits of the
 * nullness annotations in particular will outweigh the costs. (And it's worth noting that we have
 * released multiple ListenableFuture.class files that are not byte-for-byte compatible even from
 * the beginning, thanks to using different `-source -target` values for compiling our `-jre` and

clusters via the ILM transition feature. This will allow transitioning of older objects to a different cluster or the public cloud by setting up transition rules in the bucket lifecycle configuration. This feature enables applications to optimize storage costs by moving less frequently accessed data to a cheaper storage without compromising accessibility of data.

To transition objects in a bucket to a destination bucket on a different cluster, applications need to specify a transition tier defined...

			}
		}
	}
	// Update the data cache.
	hosts.expire = now.Add(cacheMaxAge)
	hosts.path = hp
	hosts.byName = hs
	hosts.byAddr = is
	hosts.mtime = mtime
	hosts.size = size
}

// lookupStaticHost looks up the addresses and the canonical name for the given host from /etc/hosts.
func lookupStaticHost(host string) ([]string, string) {
	hosts.Lock()
	defer hosts.Unlock()
	readHosts()
	if len(hosts.byName) != 0 {

namespace mlir {
namespace TFL {
namespace tac {

// Cost attribute string on the TFL dialect.
constexpr char kCost[] = "tac.cost";

inline void UpdateCost(Operation* op, float cost, OpBuilder* builder) {
  op->setAttr(kCost, builder->getF32FloatAttr(cost));
}

// Get the cost annotated with kCost.
inline bool GetCostOnOp(Operation* op, float* cost) {
  auto cost_type = op->getAttrOfType<FloatAttr>(kCost);

// RUN: tac-opt-all-backends -tfl-get-op-cost %s -split-input-file -verify-diagnostics | FileCheck %s

func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  // CHECK: tac.cost = 7.864320e+05
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>