HANDLE_TF_REF_TYPE(DoubleRef, DOUBLE_REF, "f64ref")
HANDLE_TF_REF_TYPE(Uint4Ref, UINT4_REF, "uint4ref")
HANDLE_TF_REF_TYPE(Int4Ref, INT4_REF, "int4ref")
HANDLE_TF_REF_TYPE(Uint8Ref, UINT8_REF, "uint8ref")
HANDLE_TF_REF_TYPE(Int8Ref, INT8_REF, "int8ref")
HANDLE_TF_REF_TYPE(Uint16Ref, UINT16_REF, "uint16ref")
HANDLE_TF_REF_TYPE(Int16Ref, INT16_REF, "int16ref")
HANDLE_TF_REF_TYPE(Uint32Ref, UINT32_REF, "uint32ref")
HANDLE_TF_REF_TYPE(Int32Ref, INT32_REF, "int32ref")

def TF_Uint32Ref : TF_TensorFlowType<"Uint32Ref", "ui32ref">;
def TF_Uint64Ref : TF_TensorFlowType<"Uint64Ref", "ui64ref">;

// Quantized reference types
def TF_Qint8Ref : TF_TensorFlowType<"Qint8Ref", "qint8ref">;
def TF_Qint16Ref : TF_TensorFlowType<"Qint16Ref", "qint16ref">;
def TF_Qint32Ref : TF_TensorFlowType<"Qint32Ref", "qint32ref">;
def TF_Quint8Ref : TF_TensorFlowType<"Quint8Ref", "quint8ref">;

    public final inner class kotlin/jvm/internal/Ref$IntRef
}

public final class ContextKt

public final class Foo {
    // source: 'context.kt'
    public method <init>(): void
    private final static method test$call(p0: Foo, p1: java.lang.String, p2: kotlin.jvm.internal.Ref$IntRef, p3: int): void
    public final method test(p0: java.lang.String): void

public final class CodeFragment {
    // source: 'fragment.kt'
    public method <init>(): void
    public final static method generated_for_debugger_fun(p0: kotlin.jvm.internal.Ref$IntRef): void
    public final inner class kotlin/jvm/internal/Ref$IntRef

public final class CodeFragment {
    // source: 'fragment.kt'
    public method <init>(): void
    public final static method run(p0: kotlin.jvm.internal.Ref$IntRef): void
    public final inner class kotlin/jvm/internal/Ref$IntRef
}

the standard dialect. The extra defined types are specific to TensorFlow: `QINT`
types like !tf_type.qint8 (etc), `QUINT` types like !tf_type.quint8, all of the
`REF` types like !tf_type.uint8ref, as well as !tf_type.string,
!tf_type.resource, and !tf_type.variant which correspond to the tensorflow types
of the same name.

### Example:

Below is an example of a function operating on the TensorFlow dialect:

unorderedHash$1 extends scala.runtime.AbstractFunction1 implements scala.Serializable { private final scala.runtime.IntRef a$1; private final scala.runtime.IntRef b$1; private final scala.runtime.IntRef n$1; private final scala.runtime.IntRef c$1; public void MurmurHash3$$anonfun$unorderedHash$1(scala.runtime.IntRef, scala.runtime.IntRef, scala.runtime.IntRef, scala.runtime.IntRef); } scala/util/hashing/MurmurHash3$$anonfun$orderedHash$1.class package scala.util.hashing; public final synchronized...