resyncPeriod   = time.Duration(10 * time.Minute)
	retryPeriod    = 5 * time.Second
	maxRetryPeriod = 180 * time.Second
	backoffFactor  = 2.0 // Introduce a backoff multiplier as jitter factor
)

// nodeResourcesController collects resource information from all registered
// plugins and synchronizes that information with ResourceSlice objects.
type nodeResourcesController struct {

```
~ mc admin config set alias/ scanner
KEY:
scanner  manage namespace scanning for usage calculation, lifecycle, healing and more

ARGS:
delay     (float)     scanner delay multiplier, defaults to '10.0'
max_wait  (duration)  maximum wait time between operations, defaults to '15s'
cycle     (duration)  time duration between scanner cycles
```

/// elements of the divisor are non-zero.

/// Similarly, if the op is tf.MulNoNan and the multiplier is a constant tensor
/// (with all the elements of any allowed type: float or complex), rewrite the
/// op to the multiplier if all the elements of the multiplier are zero and to
/// tf.Mul if all the elements of the multiplier are non-zero.

/// Replace the given op with an op of type `RetT`. Upon calling

                  of priority in case of multiple matches. A pool with no priority
                  set will be used only if the pools with priority can't be used.
                  If multiple matching IPAddressPools are available it will check
                  for the availability of IPs sorting the matching IPAddressPools
                  by priority, starting from the highest to the lowest. If multiple

        dyn_cast_or_null<TFL::MulOp>(fc_op.getInput().getDefiningOp());
    if (!mul_op) return failure();
    if (mul_op.getFusedActivationFunction() != "NONE") return failure();

    // Don't match muls where the multiplier constant is not 1D.
    {
      auto multiplier_shape = mlir::cast<ShapedType>(mul_op.getRhs().getType());
      if (!multiplier_shape.hasStaticShape()) return failure();

* L'apprentissage profond (ou **Deep Learning**) : est un sous-domaine du **Machine Learning**, donc les mêmes raisons s'appliquent. Avec la différence qu'il n'y a pas une unique feuille de calcul de nombres à multiplier, mais une énorme quantité d'entre elles, et dans de nombreux cas, on utilise un processeur spécial pour construire et...

			//
			//   for i := 0; i < n; i++ {
			//       d := bLimbs[i]
			//       T[n+i] = addMulVVW(T[i:n+i], aLimbs, d)
			//   }
			//
			// where d is a digit of the multiplier, T[i:n+i] is the shifted
			// position of the product of that digit, and T[n+i] is the final carry.
			// Note that T[i] isn't modified after processing the i-th digit.
			//

    if (is_depthwise) {
      // The total number of depthwise convolution output channels will be
      // equal to input channel * `depth_multiplier`.
      const int64_t multiplier = dimension_numbers.getOutputFeatureDimension() /
                                 dimension_numbers.getInputFeatureDimension();

      rewriter.replaceOpWithNewOp<TFL::DepthwiseConv2DOp>(

    /**
     * A helper method to determine the value to resume the build with {@code -rf} taking into account the edge case
     *   where multiple modules in the reactor have the same artifactId.
     * <p>
     * {@code -rf :artifactId} will pick up the first module which matches, but when multiple modules in the reactor
     *   have the same artifactId, effective failed module might be later in build reactor.

    // results for depthwise transpose convolutions with non-1 channel
    // multiplier.
    if ((kernel_output_channels / feature_group_count) != 1) {
      return rewriter.notifyMatchFailure(
          conv_op,
          "Unsupported detphwise transpose convolution with non-1 channel "
          "multiplier");
    }

    // Slicing with dynamic offsets (helper method advised)