// original const and the qbarrier that led to the quantization.
  auto fusedLoc = rewriter.getFusedLoc(
      {qbarrier.getArg().getDefiningOp()->getLoc(), qbarrier.getLoc()});
  auto newConstOp = rewriter.create<arith::ConstantOp>(
      fusedLoc, newConstValueType, cast<TypedAttr>(newConstValue));
  rewriter.replaceOpWithNewOp<StorageCastOp>(qbarrier, qbarrier.getType(),
                                             newConstOp);

  populateWithGenerated(patterns);
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    signalPassFailure();
  }
}

}  // namespace

// Creates an instance of the TensorFlow dialect PostQuantize pass.
std::unique_ptr<OperationPass<func::FuncOp>> CreatePostQuantizePass() {
  return std::make_unique<PostQuantizePass>();
}

  // TODO: b/310291615 - find a better way for multi-platform support.
  if (call_op_has_platform_index_arg) {
    arg_mapper.map(func_op.getArgument(0),
                   builder.create<mhlo::ConstantOp>(
                       func_op.getLoc(), builder.getI16IntegerAttr(0)));
  }
  for (auto [func_arg, operand] : llvm::zip_equal(
           func_op.getArguments().take_back(call_op.getNumOperands()),

  node->AddAttr(kXlaInternalScopeAttr, scope);
}

// NB! We append a new scope as suffix to the _XlaInternalScope attribute
// instead of overriding the old value.  In other words, appending scope B to
// scope A creates the conjunction of the scopes A and B (i.e, A & B) and,
// in effect, the node gets both the old and new scopes.  As a unique scope
// disallows a node being merged with nodes in other scopes, the scope

}

}  // namespace

Status MapFunctionSignaturesFromTFSavedModelMLIR(
    mlir::ModuleOp module,
    llvm::function_ref<void(const TFRTSavedModelSignatureInfo&)> map_fn) {
  // Create bound inputs for each functions.
  mlir::SymbolTable symbol_table(module);
  tensorflow::Status status = absl::OkStatus();
  module.walk([&symbol_table, map_fn, &status](mlir::func::FuncOp func) {

        func.emitError() << "Failed to duplicate values for the stateful op\n";
        return signalPassFailure();
      }
    }
  }
}

}  // namespace

/// Creates an instance of the TensorFlow Lite dialect SplitMergedOperands
/// pass.
std::unique_ptr<OperationPass<func::FuncOp>> CreateSplitMergedOperandsPass() {
  return std::make_unique<SplitMergedOperandsPass>();
}

  AddShapeLegalizationPasses(pm);
  pm.addPass(createReplaceStablehloOpsInMainFunctionWithXlaCallModuleOpsPass());
  // ReplaceStablehloOpsInMainFunctionWithXlaCallModuleOpsPass may create
  // duplicate constants. Add canonicalizer to deduplicate.
  pm.addNestedPass<func::FuncOp>(mlir::createCanonicalizerPass());
  pm.addPass(TF::CreateXlaCallModuleSerializationPass());
}

#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/public/session_options.h"
#include "tsl/platform/statusor.h"

#define DEBUG_TYPE "run-tf-graph-optimization"

namespace tensorflow {
namespace {
// Creates a pass to convert MLIR to Graph, run user-specified Graph
// Optimization Passes and convert back to MLIR.
// Constraints: This pass expects that all operations in the MLIR module either

  if (auto block_arg =
          mlir::dyn_cast<BlockArgument>(read_var_op.getResource())) {
    if (block_arg.getOwner() != replicate_block) return;

    OpBuilder builder(shape_op);
    auto new_shape_op = builder.create<TF::VariableShapeOp>(
        shape_op.getLoc(), shape_op.getType(),
        replicate_op.GetReplicaOperandForBlockArgument(block_arg,
                                                       /*replica=*/0));

                       op->getOperands(), result_types, op->getAttrs());
  for (int i = 0; i < op->getNumRegions(); ++i) {
    state.addRegion();
  }
  Operation* new_op = builder.create(state);
  for (const auto& indexed_regions : llvm::enumerate(op->getRegions())) {
    Region& region = op->getRegion(indexed_regions.index());
    IRMapping mapping;
    indexed_regions.value().cloneInto(&region, mapping);