// lots of varint sizes.
	//
	// Add 1 because we might also write a traceAllocFreeTypesBatch byte.
	var flushed bool
	w, flushed = w.ensure(1 + maxBytes)
	if flushed {
		// Annotate the batch as containing types.
		w.byte(byte(traceAllocFreeTypesBatch))
	}

	// Emit type.
	w.varint(uint64(node.id))
	w.varint(uint64(uintptr(unsafe.Pointer(typ))))
	w.varint(uint64(typ.Size()))

	// replicas is the number of Pods created by the StatefulSet controller.
	Replicas int32 `json:"replicas" protobuf:"varint,2,opt,name=replicas"`

	// readyReplicas is the number of pods created for this StatefulSet with a Ready Condition.
	ReadyReplicas int32 `json:"readyReplicas,omitempty" protobuf:"varint,3,opt,name=readyReplicas"`

  %1 = "tf.AddN"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<*xi32>>>, tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<*xi32>>>

      f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.OptionalGetValue"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<f32>
  %3 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  return %2, %3 : tensor<f32>, tensor<!tf_type.variant<tensor<f32>>>
}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {

	HandSize int32 `json:"handSize" protobuf:"varint,2,opt,name=handSize"`

	// `queueLengthLimit` is the maximum number of requests allowed to
	// be waiting in a given queue of this priority level at a time;
	// excess requests are rejected.  This value must be positive.  If
	// not specified, it will be defaulted to 50.
	// +optional
	QueueLengthLimit int32 `json:"queueLengthLimit" protobuf:"varint,3,opt,name=queueLengthLimit"`
}

    }

    private static void assertAttributes(GradleModuleMetadata.Variant variant, Map<String, Object> expected) {
        assert variant.attributes.size() == expected.size()
        expected.each { attribute, value ->
            assert variant.attributes.containsKey(attribute)
            assert variant.attributes[attribute] == value
        }
    }

    void assertNoDependencies() {

        when:
        variant.artifacts

        then:
        1 * rule.execute(_)

        when:
        variant.artifacts
        variant.artifacts
        variant.artifacts

        then:
        0 * rule.execute(_)

        where:
        metadataType | metadata
        "maven"      | mavenComponentMetadata()
        "ivy"        | ivyComponentMetadata()

    return VariantBinaryAddFunc(
        cc_ctx, cc_a.scalar<Variant>()(), cc_b.scalar<Variant>()(),
        cc_out->scalar<Variant>().data(), binary_add_func);
  } else {
    binary_add_func(ctx, a, b, out);
    return cc_ctx->status();
  }
}

static Status VariantBinaryAddFunc(
    ::tensorflow::OpKernelContext* cc_ctx, const Variant& a, const Variant& b,
    Variant* out,

    %2 = "tf.Identity"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> (tensor<!tf_type.variant<tensor<f32>>>)
    "tf.Identity"(%2) : (tensor<!tf_type.variant<tensor<f32>>>) -> (tensor<!tf_type.variant<tensor<f32>>>)
    tf_device.return
  }) {cluster_attr = "cluster_attr"} : () -> ()
  func.return
}

  %outputs_60 =  "tf.Identity"(%outputs_58) {device = ""} : (tensor<*x!tf_type.variant>) -> tensor<*x!tf_type.variant>
  return %outputs_34, %outputs_36, %outputs_44, %outputs_60, %arg4, %arg5 : tensor<*xi32>, tensor<*xi32>, tensor<*xi32>, tensor<*x!tf_type.variant>, tensor<!tf_type.variant>, tensor<?xf32>
}