func::FuncOp func = getOperation();
  if (failed(RunOnRegion(&func.getBody()))) return signalPassFailure();

  func.walk([&](Operation* op) {
    for (Region& region : op->getRegions()) {
      if (failed(RunOnRegion(&region))) return signalPassFailure();
    }
  });
}

}  // anonymous namespace

std::unique_ptr<OperationPass<func::FuncOp>>
CreateRewriteTPUEmbeddingOpsPass() {

// Adds a new input name to the `inputs` field of the `tf.entry_function`
// attribute if the attribute exist in the given function. Otherwise, no
// attribute is modified.
void AddEntryFunctionInput(StringRef input_name, func::FuncOp func_op);

}  // namespace quant
}  // namespace mlir

limitations under the License.
==============================================================================*/
include "mlir/Pass/PassBase.td"

def InputLoweringMetricsPass : Pass<"input-lowering-metrics-pass", "mlir::func::FuncOp"> {

  let summary = "Collects various metrics about the input to the lowering "
                "portion of the bridge. This is a logical no-op.";

  let description = [{

==============================================================================*/
include "mlir/Pass/PassBase.td"

def VerifyInputDialectToExecutorPass : Pass<"verify-input-dialect-to-executor-pass", "mlir::func::FuncOp"> {
  let summary = "Verify that TF dialect to executor converter receives the correct input.";
  let description = [{

    return true;
  return false;
}

class TflToStablehloPass
    : public mlir::PassWrapper<TflToStablehloPass,
                               mlir::OperationPass<mlir::func::FuncOp>> {
 public:
  explicit TflToStablehloPass() : PassWrapper() {}
  StringRef getArgument() const final { return "tfl-parse-stablehlo-ops"; }
  StringRef getDescription() const final {

void DecomposeTFOpsPass::ApplyCanonicalization() {
  func::FuncOp func = getOperation();
  RewritePatternSet patterns(&getContext());

  populateWithGenerated(patterns);
  populateCanonicalizationPatterns(func, patterns);

  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

LogicalResult DecomposeTFOpsPass::RewriteUnregisteredTFOps() {
  func::FuncOp func = getOperation();

#include "tsl/platform/env.h"
#include "tsl/platform/logging.h"
#include "tsl/platform/status.h"

namespace tensorflow {
namespace quantization {
namespace {

using ::mlir::func::FuncOp;
using ::mlir::tf_saved_model::GetInitializerFunction;
using ::mlir::tf_saved_model::kTfSavedModelInitializerRestoreType;

// Adds the tensor that initializes the variable through the provided

      } else if (auto const_op = llvm::dyn_cast<TF::ConstOp>(op)) {
        return !IsIllegalType(const_op.getOutput().getType());
      }
      // The FuncOp type can contain types that the op's operand and result
      // types do not contain.
      if (auto func = dyn_cast<func::FuncOp>(op)) {
        if (!converter_.isSignatureLegal(func.getFunctionType())) return false;
      }
      return converter_.isLegal(op);
    });
  }

    Operation *op = blockArg.getOwner()->getParentOp();

    // If we're in the first block, then the argument to that block is the
    // one we're looking for.
    if (auto func = cast<func::FuncOp>(op)) {
      if (blockArg.getOwner()->isEntryBlock()) {
        *out = blockArg;
        return success();
      }
    }

    // If we're in an inner block, the we have to find all ops that branch

    auto module_op = call_op->getParentOfType<ModuleOp>();
    SymbolTable symbol_table(module_op);

    auto f_name = f_attr.getValue();
    func::FuncOp float_func =
        dyn_cast<func::FuncOp>(symbol_table.lookup(f_name));
    if (!float_func) {
      return failure();
    }
    rewriter.setInsertionPointAfter(call_op);