//
// When is_training is set to true, the given variance and mean are not used.
// In above calculation, they are replaced by new values. These new mean and
// variance are calculated as following:
// new_mean = mean(x, axis=[0, 1, 2])
// new_variance = mean(squared_difference(x, new_mean), axis=[0, 1, 2])
//
// The DDR rule for the is_training equals true case is as following:

// the specified mean.
func fastexprand(mean int) int32 {
	// Avoid overflow. Maximum possible step is
	// -ln(1/(1<<randomBitCount)) * mean, approximately 20 * mean.
	switch {
	case mean > 0x7000000:
		mean = 0x7000000
	case mean == 0:
		return 0
	}

	// Take a random sample of the exponential distribution exp(-mean*x).

  // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e-03> : tensor<f32>
  // CHECK:  %[[MEAN:.*]] = "tf.Mean"(%arg0, %[[CST]]) <{keep_dims = false}> : (tensor<1x1x6x2xf32>, tensor<3xi32>) -> tensor<2xf32>
  // CHECK:  %[[SQ:.*]] = "tf.SquaredDifference"(%arg0, %[[MEAN]]) : (tensor<1x1x6x2xf32>, tensor<2xf32>) -> tensor<1x1x6x2xf32>
  // CHECK:  %[[MEAN0:.*]] = "tf.Mean"(%[[SQ]], %[[CST]]) <{keep_dims = false}> : (tensor<1x1x6x2xf32>, tensor<3xi32>) -> tensor<2xf32>

        Returns:
          A map of: output key -> output result.
        """
        scale = [1.0] * self.out_channel_size
        offset = [0.5] * self.out_channel_size
        mean, variance = scale, offset
        out = nn_ops.depthwise_conv2d_native(
            input_tensor,
            self.filters,
            strides=[1, 2, 2, 1],
            dilations=[1, 1, 1, 1],

      "options": {
        "colorMode": "value",
        "graphMode": "none",
        "justifyMode": "auto",
        "orientation": "auto",
        "reduceOptions": {
          "calcs": [
            "mean"
          ],
          "fields": "",
          "values": false
        },
        "showPercentChange": false,
        "text": {},
        "textMode": "auto",
        "wideLayout": true
      },

			snapshot := *v
			req.GetBody = func() (io.ReadCloser, error) {
				r := snapshot
				return io.NopCloser(&r), nil
			}
		default:
			// This is where we'd set it to -1 (at least
			// if body != NoBody) to mean unknown, but
			// that broke people during the Go 1.8 testing
			// period. People depend on it being 0 I
			// guess. Maybe retry later. See Issue 18117.
		}
		// For client requests, Request.ContentLength of 0

	// initialize expression/key size: a zero value indicates expr/key doesn't fit on a single line
	size := 0

	// We use the ratio between the geometric mean of the previous key sizes and
	// the current size to determine if there should be a break in the alignment.
	// To compute the geometric mean we accumulate the ln(size) values (lnsum)
	// and the number of sizes included (count).
	lnsum := 0.0
	count := 0

	// print all list elements

}
REGISTER_GRADIENT_OP("Sum", SumGrad);

Status MeanGrad(const Scope& scope, const Operation& op,
                const std::vector<Output>& grad_inputs,
                std::vector<Output>* grad_outputs) {
  // The Mean gradient is just like the Sum gradient, except that
  // all gradients are also divided by the size of reduced groups.
  auto sum_grad = SumGradHelper(scope, op, grad_inputs);

}

// domain represents the domain of a variable pair in which a set
// of relations is known. For example, relations learned for unsigned
// pairs cannot be transferred to signed pairs because the same bit
// representation can mean something else.
type domain uint

const (
	signed domain = 1 << iota
	unsigned
	pointer
	boolean
)

var domainStrings = [...]string{
	"signed", "unsigned", "pointer", "boolean",
}

====

However:

- Gradle only supports verification of signatures published on remote repositories as ASCII-armored PGP files
- Not all artifacts are published with signatures
- A good signature doesn't mean that the signatory was legit

As a consequence, signature verification will often be used alongside checksum verification.

.About expired keys
--