if (!parent_op) return false;
  return HasOutsideCompilationAncestor(parent_op);
}

Operation* ApplyXlaHostTransferAttr(Operation* op, OpBuilder& builder) {
  op->setAttr("_xla_has_host_transfer", builder.getBoolAttr(true));
  return op;
}

// Creates a tf._XlaSendFromHost or tf._XlaSendFromHostV2 op. If device ordinal
// is present, a tf._XlaSendFromHostV2 op is created instead.

      /*cell_clip*/ builder->getF32FloatAttr(10.0),
      /*proj_clip*/ builder->getF32FloatAttr(0.0),
      /*time_major*/ builder->getBoolAttr(time_majored),
      /*asymmetric_quantize_inputs=*/mlir::BoolAttr(),
      /*diagonal_recurrent_tensors=*/builder->getBoolAttr(indy),
      /*input_to_input_intermediate=*/mlir::TypeAttr(),
      /*input_to_forget_intermediate=*/mlir::TypeAttr(),

        (lhs_contracting_dims[0] == lhs_rank - 2 ? rewriter.getBoolAttr(true)
                                                 : rewriter.getBoolAttr(false));
    const int rhs_rank = rhs_value.getType().cast<TensorType>().getRank();
    const BoolAttr adj_y =
        (rhs_contracting_dims[0] == rhs_rank - 1 ? rewriter.getBoolAttr(true)
                                                 : rewriter.getBoolAttr(false));

    Value result = op.getResult();

    "    $2, $1.getType().cast<TensorType>().getElementType()),"
    "  $2)"
    >;

def CheckHasResourceSubtype : Constraint<CPred<"HasResourceSubtype($0)">>;

def CreateConstBoolAttrFalse : NativeCodeCall<"$_builder.getBoolAttr(false)">;

def CreateTFReadVariableOpFromResourceHandle : NativeCodeCall<
    "$_builder.create<TF::ReadVariableOp>("
    "$0.getLoc(), GetResourceSubtype($1), $1)">;

def CreateTFSelectOp: NativeCodeCall<

                      llvm::ArrayRef<int64_t>{
                          rhs_dot_dimensions_info.FlattenedOutDimensionSize()});
  BoolAttr false_attr = rewriter.getBoolAttr(false);
  auto matmul = rewriter.create<TFL::BatchMatMulOp>(
      loc, RankedTensorType::get(matmul_shape, result_type.getElementType()),
      lhs_flattend, rhs_flattend, /*adj_x*/ false_attr, /*adj_y*/ false_attr,

  OpBuilder builder(func);
  bool is_signed = quant_specs_.IsSignedInferenceType();
  IntegerAttr num_bits =
      builder.getI32IntegerAttr(quant_specs_.GetQuantizationTypeWidth());
  BoolAttr narrow_range = builder.getBoolAttr(false);

  auto add_quantize_op = [&](Location loc, Type input_type, Block* block,
                             Block::iterator insertion_point, Value arg,
                             int i) {

      {
        ::mlir::Value x_value = (*x.begin());
        mean_op_1 = rewriter.create<TF::MeanOp>(
            odsLoc, new_mean_type, x_value, reduce_dim_op,
            /*keep_dims=*/rewriter.getBoolAttr(false));
      }

      ::mlir::TF::SquaredDifferenceOp square_diff_op;
      {
        ::mlir::Value tblgen_value_0 = (*x.begin());

def CreateTFShapeOp : NativeCodeCall<
    "$_builder.create<TF::ShapeOp>($0.getLoc(), $1, $2)">;

def IsI32 : NativeCodeCall<
    "$_builder.getBoolAttr(getElementTypeOrSelf($0.getType()).isInteger(32))">;

def CreateTFCastOpF32 : NativeCodeCall<
    "CreateTFCastOpF32(&$_builder, $0.getLoc(), $1, $2)">;

def CreateTFCastOpI32 : NativeCodeCall<

  OpBuilder builder(func);
  bool is_signed = quant_specs_.IsSignedInferenceType();
  IntegerAttr num_bits =
      builder.getI32IntegerAttr(quant_specs_.GetQuantizationTypeWidth());
  BoolAttr narrow_range = builder.getBoolAttr(false);

  auto add_quantize_op = [&](Location loc, Type input_type, Block* block,
                             Block::iterator insertion_point, Value arg,
                             int i) {

                                               /*type=*/2);
  auto send = builder.create<SendOp>(
      loc, token.getType(), operand, token, channel_handle,
      /*is_host_transfer=*/builder.getBoolAttr(true));

  SetFrontendAttributes(send, index, key, operand.getType(),
                        /*device_to_host=*/true, host_handler_name);

  SetOpSharding(send, manual_sharding);

  return send.getResult();