graph_(graph),
        tf_dialect_(tf_dialect),
        symbol_table_(*symbol_table) {
    graph_->ToGraphDef(&graphdef_);
  }

  Status AddArgumentNode(BlockArgument arg, unsigned index,
                         llvm::StringRef name);
  Status AddFetchNode(FuncOp function, mlir::tf_executor::FetchOp fetch,
                      llvm::ArrayRef<llvm::StringRef> names);
  Status AddInstructionNode(Operation* inst);

        graph_(graph),
        tf_dialect_(tf_dialect),
        symbol_table_(*symbol_table) {
    graph_->ToGraphDef(&graphdef_);
  }

  Status AddArgumentNode(BlockArgument arg, unsigned index,
                         llvm::StringRef name);
  Status AddFetchNode(FuncOp function, mlir::tf_executor::FetchOp fetch,
                      llvm::ArrayRef<llvm::StringRef> names);
  Status AddInstructionNode(Operation* inst);

// operation of which use is an operand allows for shape refinement without
// a cast.
bool NeedsCastBack(OpOperand& use, Dialect* tf_dialect) {
  return use.getOwner()->getDialect() != tf_dialect &&
         !IsSupportedNonTFOp(use.getOwner());
}

TensorType CreateTensorType(std::optional<llvm::ArrayRef<int64_t>> shape,
                            Type element_type) {

      enabled_op_types_.emplace(OpType::kSelectTf);
    }
    if (toco_flags.allow_custom_ops()) {
      enabled_op_types_.emplace(OpType::kCustomOp);
    }
    tf_dialect_ =
        module.getContext()->getOrLoadDialect<mlir::TF::TensorFlowDialect>();
    tfl_dialect_ = module.getContext()
                       ->getOrLoadDialect<mlir::TFL::TensorFlowLiteDialect>();
    vhlo_dialect_ =

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "pybind11/pybind11.h"  // from @pybind11
#include "pybind11/stl.h"  // from @pybind11
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tfr/ir/tfr_ops.h"
#include "tensorflow/python/lib/core/pybind11_lib.h"

PYBIND11_MODULE(tfr_wrapper, m) {
  m.def("verify", [](std::string input) {

A. Unique TensorFlower <******@****.***> 1634142505 -0700

Russell Power <******@****.***> 1686186907 -0700

#include "tensorflow/compiler/mlir/lite/stablehlo/odml_converter/passes.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"

const char* art = R"(
  ___  ___  __  __ _       ___                     _
 / _ \|   \|  \/  | |     / __|___ _ ___ _____ _ _| |_ ___ _ _
| (_) | |) | |\/| | |__  | (__/ _ \ ' \ V / -_) '_|  _/ -_) '_|

void LaunchToDeviceAttributePass::runOnOperation() {
  const Dialect* tf_dialect = getContext().getLoadedDialect("tf");
  if (!tf_dialect) {
    getOperation().emitError() << "'tf' dialect is not registered";
    return signalPassFailure();
  }

  auto result = getOperation().walk([&tf_dialect](tf_device::LaunchOp launch) {
    if (failed(HoistOpsAndAnnotateWithDevice(tf_dialect, launch)))
      return WalkResult::interrupt();

def TFL_DimensionType : I32EnumAttr<
    "DimensionType", "dimension_type", [TFL_DT_Dense, TFL_DT_SparseCSR]> {
  let genSpecializedAttr = 0;
  let cppNamespace = "::mlir::TFL";
}

def TFL_DimensionTypeAttr : EnumAttr<TFL_Dialect, TFL_DimensionType,
    "dimension_type_attr"> {
  let convertFromStorage = "$_self";
}

// Allowed activation function cases
// These should match the ActivationFunctionType enum in TFLite schema.