anage/deploy-minio-multi-node-multi-drive.html ## rootUser: "" rootPassword: "" ## Use existing Secret that store following variables: ## ## | Chart var | .data.<key> in Secret | ## |:-------- ## | rootUser | rootUser | ## | rootPassword | rootPassword | ## ## All mentioned variables will be ignored in values file. ## .data.rootUser and .data.rootPassword are mandatory, ## others depend on enabled status of corresponding sections. existingSecret: "" ## Directory on the MinIO pof certsPath: "/etc/minio/certs/"...

			// with dead end because tcp-keepalive is not fired when there is data in the socket buffer.
			//    https://blog.cloudflare.com/when-tcp-sockets-refuse-to-die/
			// This is a sensitive configuration, it is better to set it to high values, > 60 secs since it can
			// affect clients reading data with a very slow pace  (disappropriate with socket buffer sizes)
			if opts.UserTimeout > 0 {

    return await http_exception_handler(request, exc)


@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request, exc):
    print(f"OMG! The client sent invalid data!: {exc}")
    return await request_validation_exception_handler(request, exc)


@app.get("/items/{item_id}")
async def read_item(item_id: int):
    if item_id == 3:

func loadClusterUsageObjectMetrics(ctx context.Context, m MetricValues, c *metricsCache) error {
	dataUsageInfo, err := c.dataUsageInfo.Get()
	if err != nil {
		metricsLogIf(ctx, err)
		return nil
	}

	// data usage has not captured any data yet.
	if dataUsageInfo.LastUpdate.IsZero() {
		return nil
	}

	var (
		clusterSize               uint64
		clusterBuckets            uint64
		clusterObjectsCount       uint64

Many packages that simplify it a lot have to make many compromises with the data model, database, and available features. And some of these packages that simplify things too much actually have security flaws underneath.

---

**FastAPI** doesn't make any compromise with any database, data model or tool.

It gives you all the flexibility to choose the ones that fit your project the best.

    private HtmlResponse asIndexPage(final LoginForm form) {
        if (form != null) {
            form.clearSecurityInfo();
        }
        return asHtml(virtualHost(path_Login_IndexJsp)).renderWith(data -> {
            RenderDataUtil.register(data, "notification", fessConfig.getNotificationLogin());
            saveToken();
        });
    }

    @Execute
    public HtmlResponse login(final LoginForm form) {

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,

https://docs.minio.io/docs/distributed-minio-quickstart-guide ## rootUser: "" rootPassword: "" ## Use existing Secret that store following variables: ## ## | Chart var | .data.<key> in Secret | ## |:-------- ## | rootUser | rootUser | ## | rootPassword | rootPassword | ## ## All mentioned variables will be ignored in values file. ## .data.rootUser and .data.rootPassword are mandatory, ## others depend on enabled status of corresponding sections. existingSecret: "" ## Directory on the MinIO pof certsPath: "/etc/minio/certs/"...

  // shape is explicitly requested where possible.
  int (*num_dims)(void* data, TF_Status* status);

  // Computes the axis length at `dim_index`.
  int64_t (*dim)(void* data, int dim_index, TF_Status* status);

  void (*deallocator)(void* data);

  // Summarizes the value of this tensor. The caller takes ownership of the

                                                void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
  std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);

  status->status = absl::OkStatus();