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()),

// producers before consumers, we can construct the set of reaching operations
// by taking the union of the operations reaching the input nodes.  These
// "reaching resource operations" can then be used to create the pairs of
// incompatible nodes using `IsEdgeSafe`.

#include "tensorflow/compiler/jit/resource_operation_safety_analysis.h"

#include "absl/container/flat_hash_set.h"
#include "absl/memory/memory.h"

  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

// not revisited in future calls to ScanForValue, so callers must take
// care to order their calls.
//
// Useful for merging multiple sorted lists in O(n) time.
class SinglePassSearch {
 public:
  // Creates a SinglePassSearch object that can be used to search in `values`.
  // Does not take ownership of `values`. `values` must outlive this.
  // `values` must be sorted.
  explicit SinglePassSearch(absl::Span<int const> values)

  // attribute of the MLIR op. The assigned device might be remote, not
  // available during compilation or compilation only device for on demand
  // execution which may create a recursion if used for constant folding.
  constexpr char kHostCpu[] = "/job:localhost/replica:0/task:0/CPU:0";
  TFE_OpSetDevice(op, kHostCpu, status);
  RETURN_FAILURE_IF_ERROR(status);

}

}  // 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