<sign>            ::= \"+\" | \"-\" <signedNumber>    ::= <number> | <sign><number> <suffix>          ::= <binarySI> | <decimalExponent> | <decimalSI> <binarySI>        ::= Ki | Mi | Gi | Ti | Pi | Ei\n\n\t(International System of units; See: http://physics.nist.gov/cuu/Units/binary.html)\n\n<decimalSI>       ::= m | \"\" | k | M | G | T | P | E\n\n\t(Note that 1024 = 1Ki but 1000 = 1k; I didn't choose the capitalization.)\n\n<decimalExponent> ::= \"e\" <signedNumber> | \"E\" <signedNumber> ```\n\nNo...

>>> tf.nn.elu(0.0)
<tf.Tensor: shape=(), dtype=float32, numpy=0.0>
>>> tf.nn.elu(-1000.0)
<tf.Tensor: shape=(), dtype=float32, numpy=-1.0>

See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
](http://arxiv.org/abs/1511.07289)
  }];

  let arguments = (ins
    TF_FloatTensor:$features
  );

  let results = (outs
    TF_FloatTensor:$activations
  );

        `tf.keras.optimizers.Optimizer`/`Adam`/etc will point to the new
        optimizers, and the previous generation of optimizers will be moved to
        `tf.keras.optimizers.legacy.Optimizer`/`Adam`/etc.
    *   Added L2 unit normalization layer `tf.keras.layers.UnitNormalization`.
    *   Added `tf.keras.regularizers.OrthogonalRegularizer`, a new regularizer
        that encourages orthogonality between the rows (or columns) or a weight

					HostPath: &core.HostPathVolumeSource{
						Path: "/foo",
						Type: newHostPathType(string(core.HostPathDirectory)),
					},
				},
			}),
		},
		"good-volume-with-capacity-unit": {
			isExpectedFailure: false,
			volume: testVolume("foo", "", core.PersistentVolumeSpec{
				Capacity: core.ResourceList{
					core.ResourceName(core.ResourceStorage): resource.MustParse("10Gi"),
				},