SmallVector<bool, 4> removed_operand(while_op.getNumOperands(), false);
    llvm::SmallVector<Type, 4> types;
    new_operands.reserve(while_op.getNumOperands());
    new_body_yield.reserve(while_op.getNumOperands());
    types.reserve(while_op.getNumOperands());

    // Remove block arguments not used in either cond or body. This leaves the
    // block arguments of body and cond matching still.

      // Convert values.
      std::vector<Eigen::half> new_values;
      const DenseFPElementsAttr value_attr =
          mlir::cast<DenseFPElementsAttr>(op.getValue());
      new_values.reserve(value_attr.getNumElements());

      for (const float value : value_attr.getValues<float>()) {
        new_values.push_back(Eigen::half(
            std::min(std::max(value, kMinFloat16Value), kMaxFloat16Value)));

			if err != nil {
				return nil, fmt.Errorf("failed to parse percentage %q for %q resource: %w", v, k, err)
			}
			reservations[v1.ResourceName(k)] = q
		default:
			return nil, fmt.Errorf("cannot reserve %q resource", k)
		}
	}
	return &reservations, nil
}

func containerDevicesFromResourceDeviceInstances(devs devicemanager.ResourceDeviceInstances) []*podresourcesapi.ContainerDevices {

Les données sont calculées chaque mois, vous pouvez lire le <a href="https://github.com/tiangolo/fastapi/blob/master/.github/actions/people/app/main.py" class="external-link" target="_blank">code source ici</a>.

      auto permuation_tensor_type =
          RankedTensorType::get({input_rank}, rewriter.getIntegerType(32));
      llvm::SmallVector<Attribute, 4> permute;
      permute.reserve(input_rank);
      // First create an identity permutation tensor.
      for (int i = 0; i < input_rank; i++) {
        permute.push_back(rewriter.getI32IntegerAttr(i));
      }

	// since metadata is always stored in memory, not disk.
	//
	// maxMemoryBytes is the maximum bytes we will store in memory, including file content,
	// non-file part values, metadata, and map entry overhead.
	//
	// We reserve an additional 10 MB in maxMemoryBytes for non-file data.
	//
	// The relationship between these parameters, as well as the overly-large and
	// unconfigurable 10 MB added on to maxMemory, is unfortunate but difficult to change

        continue;
      }

      // Collect all the quantized inputs and "clone" the matched op by these
      // inputs.
      SmallVector<Value, 4> inputs;
      inputs.reserve(quantizing_op->getNumOperands());
      for (const auto& operand : quantizing_op->getOperands()) {
        Type operand_type = operand.getType();
        if (operand_type.isa<NoneType>()) {

            this.serializer = serializer;
        }
    }

    private static class TaggedTypeSerializer<T> extends AbstractSerializer<T> {
        private static final int JAVA_TYPE = 1; // Reserve 0 for null (to be added later)
        private static final TypeInfo JAVA_SERIALIZATION = new TypeInfo(JAVA_TYPE, true, new DefaultSerializer<Object>());

			}
		}
		if claim.Status.Allocation != nil &&
			!resourceclaim.IsReservedForPod(pod, claim) &&
			resourceclaim.CanBeReserved(claim) {
			// Need to reserve it.
			return true, "need to reserve claim for pod"
		}
	}

	return false, "nothing to do"
}

func (ec *Controller) enqueueResourceClaim(logger klog.Logger, obj interface{}, deleted bool) {

                                            int index,
                                            const QuantParamsEntry &info) {
  if (info.params_size() == 0) return;

  SmallVector<APFloat, 4> min_maxs;
  min_maxs.reserve(info.params_size() * 2);
  for (const auto &param : info.params()) {
    llvm::APFloat min(param.min_max().min());
    llvm::APFloat max(param.min_max().max());
    min_maxs.push_back(min);
    min_maxs.push_back(max);