"type(self.val7) == double",
				"self.val7 == 1.0",
			},
		},
		{name: "numeric comparisons",
			obj: objs(
				int64(5),      // val1, integer type, integer value
				int64(10),     // val2, integer type, integer value
				int64(15),     // val3, integer type, integer value
				float64(10.0), // val4, number type, parsed from decimal literal
				float64(10.0), // val5, float type, parsed from decimal literal

of the coordinate system; thus translating or reflections of the coordinate
system result in the same boxes being selected by the algorithm.
The output of this operation is a set of integers indexing into the input
collection of bounding boxes representing the selected boxes.  The bounding
box coordinates corresponding to the selected indices can then be obtained
using the `tf.gather operation`.  For example:

				)
				(Const64 <typ.UInt64> [64-sdivisible64(c).k])
			)
			(Const64 <typ.UInt64> [int64(sdivisible64(c).max)])
		)

// Divisibility check for signed integers for power of two constant are simple mask.
// However, we must match against the rewritten n%c == 0 -> n - c*(n/c) == 0 -> n == c*(n/c)
// where n/c contains fixup code to handle signed n.
((Eq8|Neq8) n (Lsh8x64

    Function<Integer, Integer> adder =
        new Function<Integer, Integer>() {
          @Override
          public Integer apply(Integer from) {
            return from + holder.value;
          }
        };

    // Since holder.value is 2, applying 4 should yield 6.
    assertEquals(6, adder.apply(4).intValue());

    ListenableFuture<Integer> immediateFuture = immediateFuture(4);

    Function<Integer, Integer> adder =
        new Function<Integer, Integer>() {
          @Override
          public Integer apply(Integer from) {
            return from + holder.value;
          }
        };

    // Since holder.value is 2, applying 4 should yield 6.
    assertEquals(6, adder.apply(4).intValue());

    ListenableFuture<Integer> immediateFuture = immediateFuture(4);

					a1 = C_LCON
				}
			}
			if ((p.As == AMOVD) || isANDop(p.As) || isADDop(p.As)) && (a1 == C_LCON || a1 == C_VCON) {
				// more specific classification of 64-bit integers
				a1 = c.con64class(&p.From)
			}
		}
		if p.From.Type == obj.TYPE_MEM {
			if isMOVop(p.As) && (cmp(C_LAUTO, a1) || cmp(C_LOREG, a1)) {
				// More specific classification of large offset loads and stores.

		{Name: "Desired", Type: "integer", Description: extensionsv1beta1.DaemonSetStatus{}.SwaggerDoc()["desiredNumberScheduled"]},
		{Name: "Current", Type: "integer", Description: extensionsv1beta1.DaemonSetStatus{}.SwaggerDoc()["currentNumberScheduled"]},
		{Name: "Ready", Type: "integer", Description: extensionsv1beta1.DaemonSetStatus{}.SwaggerDoc()["numberReady"]},

func TestValidateIndexers(t *testing.T) {
	testcases := []struct {
		name          string
		indexers      *cache.Indexers
		expectedError bool
	}{
		{
			name:          "nil indexers",
			indexers:      nil,
			expectedError: false,
		},
		{
			name: "normal indexers",
			indexers: &cache.Indexers{
				"f:spec.nodeName":            emptyIndexFunc,
				"l:controller-revision-hash": emptyIndexFunc,

  // expected-error @+1 {{'tfl.one_hot' op result #0 must be tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values, but got 'tensor<*xi16>'}}
  %0 = "tfl.one_hot"(%arg0, %arg1, %arg2, %arg3) {axis = -1 : i32} : (tensor<3xi32>, tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<*xi16>

	}{
		{
			derBytes: []byte{
				0x30, 6, // SEQUENCE, 6 bytes
				0x02, 1, // INTEGER, 1 byte
				17,
				0x02, 1, // INTEGER, 1 byte
				3, // 3
			},
		}, {
			derBytes: []byte{
				0x30, 6, // SEQUENCE
				0x02, 1, // INTEGER, 1 byte
				0xff,    // -1
				0x02, 1, // INTEGER, 1 byte
				3,
			},
			expectedErrSubstr: "zero or negative",
		}, {
			derBytes: []byte{