return %3 : tensor<1x2x!quant.uniform<i8:f32, 4.000000e+00:7>>
}
// Checks that the `stablehlo.dot_general` is not converted to
// `tfl.fully_connected`. Also notice that the `stablehlo.transpose` and
// `stablehlo.uniform_quantize` are converted separately.

// CHECK: tfl.transpose
// CHECK: stablehlo.dot_general
// CHECK-NOT: tfl.fully_connected
// CHECK: tfl.quantize

// -----

    )
    converted_signature_map = save_model.get_signatures_from_saved_model(
        self._output_saved_model_path,
        signature_keys=signatures.keys(),
        tags=tags,
    )

    # The original and converted model should have the same signature map.
    self.assertAllInSet(
        list(original_signature_map.keys()), set(signatures.keys())
    )
    self.assertDictEqual(original_signature_map, converted_signature_map)

// an MLIR Module.
//
// The nodes defined in the graph are converted to a function called
// 'func_name'. All library function definitions are converted to MLIR functions
// in the module.
class GraphDefImporter : public ImporterBase {
 public:
  // Main entry point: converts the given graph to an MLIR Module.
  static absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> Convert(
      mlir::MLIRContext* context, const Graph& graph,

<pre>
*Point(p)        // same as *(Point(p))
(*Point)(p)      // p is converted to *Point
&lt;-chan int(c)    // same as &lt;-(chan int(c))
(&lt;-chan int)(c)  // c is converted to &lt;-chan int
func()(x)        // function signature func() x
(func())(x)      // x is converted to func()
(func() int)(x)  // x is converted to func() int
func() int(x)    // x is converted to func() int (unambiguous)
</pre>

<p>

}

def NameAnonymousIteratorsPass : Pass<"tf-name-anonymous-iterators", "ModuleOp"> {
  let summary = "Converts anonymous iterators to named iterators";
  let description = [{
      This converts AnonymousIterator ops to Iterator, thus giving them a name.
      For example, this will convert
        %0 = "tf.AnonymousIteratorV3"() {...}
      to
        %0 = "tf.Iterator"() {shared_name = "_iterator1", ...}

    ConversionPatternRewriter& rewriter) {
  return failure();
}

// Converts a mhlo.reduce op with a mlho binary operation into a tensorflow
// reduction operation. If the initial value can be ignored, then convert it
// into a single TfReduceOp. Otherwise, convert it into a TfReduceOp followed by
// a TfBinaryOp.
// For example:

  if (!status.ok()) {
    inst->emitError(
        Twine("failed to convert value attribute to tensor with error: " +
              status.ToString()));
    return std::nullopt;
  }

  // TensorFlow and TensorFlow Lite use different string encoding formats.
  // Convert to TensorFlow Lite format is it's a constant string tensor.
  if (tensor.dtype() == tensorflow::DT_STRING) {

	container := restful.NewContainer()
	container.Router(restful.CurlyRouter{})
	mux := container.ServeMux

	template := APIGroupVersion{
		Storage: storage,

		Creater:         scheme,
		Convertor:       scheme,
		TypeConverter:   managedfields.NewDeducedTypeConverter(),
		UnsafeConvertor: runtime.UnsafeObjectConvertor(scheme),
		Defaulter:       scheme,
		Typer:           scheme,
		Namer:           namer,

    }

    @Override
    public int hashCode() {
      return Sets.hashCodeImpl(this);
    }
  }

  /**
   * Returns a {@link Converter} that converts values using {@link BiMap#get bimap.get()}, and whose
   * inverse view converts values using {@link BiMap#inverse bimap.inverse()}{@code .get()}.
   *
   * <p>To use a plain {@link Map} as a {@link Function}, see {@link

    }

    @Override
    public int hashCode() {
      return Sets.hashCodeImpl(this);
    }
  }

  /**
   * Returns a {@link Converter} that converts values using {@link BiMap#get bimap.get()}, and whose
   * inverse view converts values using {@link BiMap#inverse bimap.inverse()}{@code .get()}.
   *
   * <p>To use a plain {@link Map} as a {@link Function}, see {@link