},

		// (InvertFlags (CMPQ a b)) == (CMPQ b a)
		// So if we want (SETL (CMPQ a b)) but we can't do that because a is a constant,
		// then we do (SETL (InvertFlags (CMPQ b a))) instead.
		// Rewrites will convert this to (SETG (CMPQ b a)).
		// InvertFlags is a pseudo-op which can't appear in assembly output.
		{name: "InvertFlags", argLength: 1}, // reverse direction of arg0

		// Pseudo-ops

(Rsh8x16 x y) => (SRAcond (SignExt8to32 x) (ZeroExt16to32 y) (CMPconst [256] (ZeroExt16to32 y)))
(Rsh8x8  x y) => (SRA (SignExt8to32 x) (ZeroExt8to32 y))

// constant shifts
// generic opt rewrites all constant shifts to shift by Const64
(Lsh32x64 x (Const64 [c])) && uint64(c) < 32 => (SLLconst x [int32(c)])
(Rsh32x64 x (Const64 [c])) && uint64(c) < 32 => (SRAconst x [int32(c)])

*   The Apiserver proxy now rewrites the URL when a service returns an absolute path with the request's host. ([#52556](https://github.com/kubernetes/kubernetes/pull/52556),[ @roycaihw](https://github.com/roycaihw))

// as in -test.v. When invoking the generated test binary (the result of
// 'go test -c') directly, however, the prefix is mandatory.
//
// The 'go test' command rewrites or removes recognized flags,
// as appropriate, both before and after the optional package list,
// before invoking the test binary.
//
// For instance, the command
//

		t.Errorf("numComments (%d) != 0 after partial resync when numEndpoints (%d) > threshold (%d)", numComments, expectedEndpoints+3, largeClusterEndpointsThreshold)
	}

	// Now force a full resync and confirm that it rewrites the older services with
	// no comments as well.
	fp.forceSyncProxyRules()
	expectedEndpoints += 3

	firstEndpoint, numEndpoints, numComments = countEndpointsAndComments(fp.iptablesData.String(), "10.0.0.0")

(Terry) Tang, Yuxin Wu, Ziyue(Louis) Lu

# Release 1.7.0

## Major Features And Improvements

*   Eager mode is moving out of contrib, try `tf.enable_eager_execution()`.
*   Graph rewrites emulating fixed-point quantization compatible with TensorFlow
    Lite, supported by new `tf.contrib.quantize` package.
*   Easily customize gradient computation with `tf.custom_gradient`.