break
		}
		v.reset(OpARM64MNEGW)
		v.AddArg2(x, y)
		return true
	}
	// match: (NEG (NEG x))
	// result: x
	for {
		if v_0.Op != OpARM64NEG {
			break
		}
		x := v_0.Args[0]
		v.copyOf(x)
		return true
	}
	// match: (NEG (MOVDconst [c]))
	// result: (MOVDconst [-c])
	for {
		if v_0.Op != OpARM64MOVDconst {
			break
		}

		{"(*Float).MantExp", Method, 5},
		{"(*Float).MarshalText", Method, 6},
		{"(*Float).MinPrec", Method, 5},
		{"(*Float).Mode", Method, 5},
		{"(*Float).Mul", Method, 5},
		{"(*Float).Neg", Method, 5},
		{"(*Float).Parse", Method, 5},
		{"(*Float).Prec", Method, 5},
		{"(*Float).Quo", Method, 5},
		{"(*Float).Rat", Method, 5},
		{"(*Float).Scan", Method, 8},
		{"(*Float).Set", Method, 5},

    TF_FloatTensor:$x
  );

  let results = (outs
    TF_FloatTensor:$y
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_NegOp : TF_Op<"Neg", [Pure, TF_CwiseUnary, TF_Involution, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes numerical negative value element-wise.";

  let description = [{
I.e., \\(y = -x\\).
  }];

tf.reverse() now takes indices of axes to be reversed. E.g. `tf.reverse(a,
[True, False, True])` must now be written as `tf.reverse(a, [0, 2])`.
`tf.reverse_v2()` will remain until 1.0 final. * `tf.mul`, `tf.sub` and `tf.neg`
are deprecated in favor of `tf.multiply`, `tf.subtract` and `tf.negative`. *
`tf.pack` and `tf.unpack` are deprecated in favor of `tf.stack` and
`tf.unstack`. * `TensorArray.pack` and `TensorArray.unpack` are getting