core.ResourcePods:                     resource.MustParse("10"),
			core.ResourceServices:                 resource.MustParse("0"),
			core.ResourceReplicationControllers:   resource.MustParse("10"),
			core.ResourceQuotas:                   resource.MustParse("10"),
			core.ResourceConfigMaps:               resource.MustParse("10"),
			core.ResourceSecrets:                  resource.MustParse("10"),

 * $ e ^ x - 1 $ if $ x < 0 $
 * $ x $ if $ x >= 0 $

Examples:

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

                  type: object
                  x-kubernetes-validations:
                  - message: only one of jwks or jwksUri can be set
                    rule: (has(self.jwksUri)?1:0)+(has(self.jwks_uri)?1:0)+(has(self.jwks)?1:0)<=1
                maxItems: 4096
                type: array
              selector:
                description: Optional.
                properties:

                  type: object
                  x-kubernetes-validations:
                  - message: only one of jwks or jwksUri can be set
                    rule: (has(self.jwksUri)?1:0)+(has(self.jwks_uri)?1:0)+(has(self.jwks)?1:0)<=1
                maxItems: 4096
                type: array
              selector:
                description: Optional.
                properties:

        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],
        keepdims=True) # Author code uses std + 1e-5 return
        super().convolution_op(inputs, (kernel - mean) / tf.sqrt(var + 1e-10))`

          },
          "revisionHistoryLimit": {
            "description": "The number of old history to retain to allow rollback. This is a pointer to distinguish between explicit zero and not specified. Defaults to 10.",
            "format": "int32",
            "type": "integer"
          },
          "selector": {
            "allOf": [
              {

		v2.AddArg2(src, mem)
		v1.AddArg3(dst, v2, mem)
		v.AddArg3(dst, v0, v1)
		return true
	}
	// match: (Move [10] dst src mem)
	// result: (MOVHstore [8] dst (MOVHUload [8] src mem) (MOVDstore dst (MOVDload src mem) mem))
	for {
		if auxIntToInt64(v.AuxInt) != 10 {
			break
		}
		dst := v_0
		src := v_1
		mem := v_2
		v.reset(OpARM64MOVHstore)
		v.AuxInt = int32ToAuxInt(8)

Class <org.gradle.internal.instantiation.generator.AsmBackedClassGenerator$ClassBuilderImpl$10> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (AsmBackedClassGenerator.java:0)