func.func @main(%arg_0: tensor<!tf_type.string> {tf_saved_model.bound_input = @__tf_saved_model_asset0}) -> () attributes {tf.entry_function = {inputs = "arg_0:0", outputs = ""}} {
        return
      }
    }
  )mlir");

  FailureOr<SmallVector<AssetFileDef>> asset_file_defs =
      ConvertAssetArgs(*module_op);

  EXPECT_TRUE(succeeded(asset_file_defs));

        // Once we migrate to all-scheduled transforms we should consider if we can avoid having this optimization and use only normalized inputs.
        //
        // Note that we are not capturing this value in the actual inputs of the work; doing so would cause unnecessary cache misses.
        // This is why the hash is captured here and not in visitIdentityInputs().

// TFRTSavedModelSignatureInfo contains the metadata for a signature in the
// savedmodel such as function name, inputs/outputs' names and types. This can
// be used to retrieve these information in a tf_saved_model module.
struct TFRTSavedModelSignatureInfo {
  llvm::StringRef func_name;

  // The following are metadata for inputs.
  llvm::ArrayRef<llvm::StringRef> input_names;
  llvm::ArrayRef<

                                PatternRewriter& rewriter) const override {
    MLIRContext* ctx = reduce_op.getContext();
    ValueRange inputs = reduce_op.getInputs();
    // Only handle binary reduce ops for now
    if (inputs.size() != 1) return failure();
    Value reduce_input = inputs[0];

    RankedTensorType reduce_type =
        reduce_op.getResultTypes()[0].cast<RankedTensorType>();

    $metadata is a serialized TPUCompileMetadataProto describing the shapes and types of the inputs to the computation, as well as a mapping onto the TPU pod topology.
    $constant_operand_indices are the indices of the inputs that are constant to the TPU program (e.g. weights in inference), the rest of the inputs are input tensors.
    constant_operand_indices is sorted in ascending order.

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-xla-validate-inputs

// expected-error @+1 {{expects no nested calls of entry functions as they prevent graph traversal in some passes from working correctly}}
func.func @nested_entry_functions() attributes {tf.entry_function = {}} {
  tf_executor.graph {

  func.func @serving_default(%arg0: tensor<1x20x20x28xf32> {tf_saved_model.index_path = ["a"]}) -> (tensor<1x40x40x28xf32> {tf_saved_model.index_path = ["b"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "c:0", outputs = "d:0"}, tf_saved_model.exported_names = ["serving_default"]} {
      %c = stablehlo.constant dense<40> : tensor<2xi32>

	"k8s.io/apiserver/pkg/admission/plugin/cel"
	"k8s.io/apiserver/pkg/authorization/authorizer"
)

var _ cel.ExpressionAccessor = &ValidationCondition{}

// ValidationCondition contains the inputs needed to compile, evaluate and validate a cel expression
type ValidationCondition struct {
	Expression string
	Message    string
	Reason     *metav1.StatusReason
}

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s


module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1660 : i32}} {
  func.func @main(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> attributes {tf.entry_function = {control_outputs = "", inputs = "args_tf_0", outputs = "Identity"}} {
    %0 = tf_executor.graph {

  func.return %1 : tensor<1x1x1x96xf32>
}
}

// CHECK: func.func @main(%arg0: tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32> attributes {tf.entry_function = {inputs = "arg0", outputs = "exp"}} {
// CHECK-NEXT:  %0 = "vhlo.logistic_v1"(%arg0) : (tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32>
// CHECK-NEXT:  %1 = "tfl.exp"(%0) : (tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32>