* java.util.function.Function java.util.function.Function}.
 *
 * <p>The {@link Functions} class provides common functions and related utilities.
 *
 * <p>See the Guava User Guide article on <a
 * href="https://github.com/google/guava/wiki/FunctionalExplained">the use of {@code Function}</a>.
 *
 * <h3>For Java 8+ users</h3>
 *

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/mlir/tfrt/function/function.h"

#include "absl/log/log.h"
#include "absl/strings/match.h"
#include "mlir/IR/OperationSupport.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project

  // to proactively transfer the result to host since the consumer op (or
  // function) of the result may still be on TPU.
  // TODO(b/194081364): remove this option once we unify servo TPU serving
  // result transfer behavior.
  bool tpu_transfer_result_to_host = false;
};

// Compile MLIR generated by tf.function in TF dialect into BEF.
Status CompileTFMLIRToBEF(const TfrtFunctionCompileOptions& options,

  assert(cost_map_.count(op) > 0);
  return cost_map_.lookup(op);
}

void CostAnalysis::AnalyzeArguments(mlir::func::FuncOp func_op) {
  // Use the max size among function inputs as the default size of dynamic
  // shaped tensors in the function.
  for (auto arg : func_op.getArguments()) {
    if (!mlir::isa<mlir::TensorType>(arg.getType())) continue;
    auto type = mlir::cast<mlir::TensorType>(arg.getType());

};

// Folds tf.IdentityOp and tf.IdentityNOp if op device and the argument devices
// from the defining ops match.
void TensorDeviceCopyConversionPass::runOnOperation() {
  func::FuncOp func_op = getOperation();

  auto should_fold_op_func = [&func_op](const Value &arg,
                                        const StringAttr &op_device) {
    // In TFRT TPU, tensor transfer is handled specifically by D2H and

          }
        }
      }
    });
  }
  // Remove `kSkipIslandOutlining` attributes.
  for (func::FuncOp func_op : getOperation().getOps<func::FuncOp>()) {
    if (func_op->hasAttr(mlir::TF::kSkipIslandOutlining)) {
      func_op->removeAttr(mlir::TF::kSkipIslandOutlining);
    }
  }
}

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>>

      return failure();

    // Fetch the callee function.
    SymbolTable symbol_table(call_op->getParentOfType<ModuleOp>());
    auto func_op =
        dyn_cast_or_null<func::FuncOp>(symbol_table.lookup(func_name));
    if (!func_op) return failure();
    // The quantized fusion should have requantize and return ops at the end.
    auto return_op = dyn_cast_or_null<func::ReturnOp>(
        func_op.getRegion().getBlocks().front().getTerminator());

      }
    }
  }
  return false;
}

// Lifts HashTable ops in the target function as function arguments and returns
// the lifted ops. These ops  will then be added to the caller function and
// passed to the target function.
LogicalResult LiftHashTableOpsToArguments(ModuleOp module_op,
                                          func::FuncOp target_func) {
  if (!llvm::hasSingleElement(target_func)) return success();

  return IsReplicatedGraph(module);
}

bool HasTPUPartitionedCallOpInModule(mlir::ModuleOp module) {
  bool has_tpu_partitioned_call = false;
  for (auto func_op : module.getOps<mlir::func::FuncOp>()) {
    func_op->walk([&](mlir::TF::TPUPartitionedCallOp op) {
      has_tpu_partitioned_call = true;
    });
    if (has_tpu_partitioned_call) break;
  }
  return has_tpu_partitioned_call;
}

                          std::unique_ptr<Graph>* graph,
                          FunctionLibraryDefinition* flib_def);

// Converts an MLIR function and adds it to a FunctionLibraryDefinition.
Status ConvertMlirFunctionToFunctionLibraryDef(mlir::func::FuncOp func,
                                               const GraphExportConfig& configs,
                                               FunctionDef* function_def);