];
}

def LegalizeWhilePass : Pass<"tfl-legalize-tf-while", "mlir::ModuleOp"> {
  let summary = "Legalize from TensorFlow While to TensorFlow Lite While.";
  let constructor = "CreateLegalizeTFWhilePass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

def LegalizeVariablesPass : Pass<"tfl-legalize-variables-tf", "mlir::ModuleOp"> {
  let summary = "Legalize TensorFlow variables to TensorFlow Lite dialect.";

    "/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;

def : Pat<(MHLO_ConstantOp:$output $value), (TF_ConstOp $value),
          [(TF_Tensor $output)]>;

//===----------------------------------------------------------------------===//
// Binary op patterns.
// Note that these are legalized from chlo.broadcast_* ops, since those are
// semantically compatible with the corresponding TF ops. Depending on
// context, getting to these ops may require some raising.

// This transformation pass converts operations in TensorFlow dialect into
// operations that are legal in the TensorFlow Lite dialect.  Operations that
// can be legalized to TensorFlow Lite dialect with simple replacements are part
// of this pass and other operations that may create extra ops should be part of
// the PrepareTF pass which should be run before this pass.  That way any

                        lower_to_xla_hlo);

  if (lower_to_xla_hlo) {
    // This pass operates on MHLO control flow ops so it should be legalized
    // after the control flow ops are legalized.
    pm.addPass(mlir::mhlo::CreateLegalizeTFCommunicationPass());

    // Everything should be MHLO after this.
    if (!allow_partial_conversion) {
      pm.addNestedPass<mlir::func::FuncOp>(

}

def TFR_TFRQuantRescaleOp : TFR_Op<"quant_rescale", [Pure]> {
  let description = [{
   The `quant_rescale` rescales the elements of the integer tensor by the
   floating-point rescale factor. This op needs to be legalized to the preferred
   operations of the backends.

    Example:

    ```mlir
    %3 = tfr.quant_rescale(%2, %1, %0) :
        (tfr.tensor, tfr.tensor, i64) -> (tfr.tensor)
    ```
  }];

==============================================================================*/

// This transformation pass prepares for legalization to the TFLite dialect by
// converting operations in TensorFlow dialect into operations that can be
// legalized to TensorFlow Lite dialect with simple replacements.  The newly
// created operations are in the TensorFlow dialect if the operation can be
// represented using a TensorFlow op.  Otherwise, TensorFlow Lite dialect op is

// RUN: tf-opt --tfl-legalize-tf-while %s -o - | FileCheck %s
// RUN: tf-opt --tfl-legalize-tf-while %s -o - --tfl-legalize-tf-while --inline='default-pipeline=''' | FileCheck %s --check-prefix=INLINE
// RUN: tf-opt --tfl-legalize-tf-while %s -o - --tfl-legalize-tf-while --inline | FileCheck %s --check-prefix=CANON

// RUN: tf-opt %s -split-input-file -tfl-legalize-variables-tf --cse | FileCheck %s

// Test for case with no session initialize op.
module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: serving_default
  func.func @serving_default(%arg0: tensor<1x10xf32> {tf_saved_model.index_path = ["x"]}) ->

// RUN: odml-to-stablehlo-opt %s --stablehlo-legalize-vhlo -split-input-file | FileCheck %s
// RUN: odml-to-stablehlo-opt --stablehlo-legalize-vhlo %s | odml-to-stablehlo-opt --vhlo-legalize-stablehlo > %t.0
// RUN: odml-to-stablehlo-opt %s > %t.1
// RUN: diff %t.0 %t.1

// CHECK-LABEL: op_tfl
func.func @op_tfl(%arg0 : tensor<f32>) -> (tensor<f32>) {
  // CHECK: %0 = tfl.add %arg0, %arg0 {fused_activation_function = "NONE"} : tensor<f32>