(HasOneUse $relu_out),
   (HasOneUse $mul_out),
   (HasOneUse $input_neg_out)]>;

// The constant folding in this pass might produce constant in the tf dialect.
// This rule is to legalize these constant to the tfl dialect.
def LegalizeConstOp : Pat<
  (TF_ConstOp ElementsAttr:$value), (TFL_ConstOp $value)>;

// Reorders adds to allow constant folding.
// Add --> Add $input, $constantA

    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count",
    "Counts the failure of legalization of ops", "op_name", "legality");

class LegalizeTF : public impl::LegalizeTFBase<LegalizeTF> {
 public:
  explicit LegalizeTF(bool legalize_chlo,
                      std::optional<StringRef> tf2xla_fallback_device_type,
                      bool prefer_tf2xla) {
    legalize_chlo_ = legalize_chlo;

// Run legalize pass and then optimize pass, and make sure some fusing is applied.
// RUN: tf-opt %s -tfl-legalize-tf -tfl-optimize | FileCheck --check-prefix=Fusing %s
// Run legalize pass and then optimize pass, and make sure some fusing is applied, but no mul->fc.
// RUN: tf-opt %s -tfl-legalize-tf -tfl-optimize='disable-fuse-mul-and-fc=true' | FileCheck --check-prefix=NoFusing %s

                               : OpBuilder::atBlockEnd(body, listener);
    }
  }];

  // Canonicalizer wasn't defined for this one. In practise, we legalize the
  // tf.IfOp to scf.If op first and then legalize it to tfl.if to reduce
  // code redundancy.
}

def TFL_WhileOp : Op<TFL_Dialect, "while", [
    DeclareOpInterfaceMethods<LoopLikeOpInterface, ["isDefinedOutsideOfLoop"]>,

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_LEGALIZEVARIABLESPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

// Attribute name to identify whether variables should be legalized to TFLite or
// not.
const char kLegalizeTflVariables[] = "tfl._legalize_tfl_variables";

bool HasSupportedElementType(Operation* op) {
  return utils::IsSupportedVariableType(op);
}

//   . Legalizes control flow operations.
//   . Decomposes compound resource operations so that the only remaining
//     operations on resource variables are resource reads/writes..
//   . Replaces resource reads/writes with function inputs/outputs and
//     eliminates the use of resource variables.
//   . Legalizes the operations to XLA HLO operations.

After serialization, this pass removes the `_entry_function` attribute from
`tf.XlaCallModule`, and removes all the serialized stablehlo functions
from the top-level module.
### `-tfe-legalize-tfg`

#include "mlir/Support/TypeID.h"  // from @llvm-project

namespace mlir {
namespace mhlo {

// Given the Operation, return True if the op is legalized with MLIR.
// Returns false if it is legalized with an old TF2XLA Kernel.
bool IsOpLegalizedWithMlir(Operation& op);

// Given the type ID, check if it's legalized with MLIR.
bool IsTypeLegalizedWithMlir(const TypeID& type_id);

// Returns true if the op is considered a dynamic padder op.

                            bool smuggle_disallowed_ops);

// This function is a common entry point for all graph optimizations that are
// not specific to any hardware. It legalizes SHLO->MHLO, does MHLO->MHLO
// optimizations by calling `AddMhloOptimizationPasses` internally, and
// legalizes MHLO->SHLO
void AddStablehloOptimizationPasses(OpPassManager& pm);

// Adds all the backend-agonstic stableHLO optimization passes

      Value value, const std::string& name, unsigned buffer_idx,
      const std::optional<BufferOffset<tflite::QuantizationParameters>>&
          quant_parameters);

  // TODO(b/137395003): Legalize tf.IfOp to TFLite dialect, and change the
  // following method to handle TFL::IfOp.
  BufferOffset<tflite::Operator> BuildIfOperator(
      mlir::TF::IfOp op, const std::vector<int32_t>& operands,