- A maximum of 100M inside a single zip is allowed. However, a reasonable limit of 100,000 files inside a single ZIP archive is recommended for best performance and memory usage trade-off.

## Content-Type

If you have a file-like object (e.g. the object returned by `open()`), you can create a generator function to iterate over that file-like object.

That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

This includes many libraries to interact with cloud storage, video processing, and others.

That "wait for something else" normally refers to <abbr title="Input and Output">I/O</abbr> operations that are relatively "slow" (compared to the speed of the processor and the RAM memory), like waiting for:

* the data from the client to be sent through the network
* the data sent by your program to be received by the client through the network

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

    *   Object metadata has now been deprecated and no longer saved to the
        SavedModel.

*   TF Core:

    *   Added `tf.config.experimental.reset_memory_stats` to reset the tracked
        peak memory returned by `tf.config.experimental.get_memory_info`.

*   `tf.data`:

    *   Added `target_workers` param to `data_service_ops.from_dataset_id` and

 * concurrently.
 */
class Dispatcher() {
  internal val lock: ReentrantLock = ReentrantLock()

  /**
   * The maximum number of requests to execute concurrently. Above this requests queue in memory,
   * waiting for the running calls to complete.
   *
   * If more than [maxRequests] requests are in flight when this is invoked, those requests will
   * remain in flight.
   */
  var maxRequests = 64

        "//tensorflow/core/distributed_runtime/rpc:rpc_rendezvous_mgr",
        "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings:str_format",
        "@local_xla//xla/tsl/c:tsl_status_internal",
    ],
    alwayslink = 1,
)

filegroup(
    name = "pywrap_required_hdrs",
    srcs = [

```

!!! tip
    The app above is a minimal and simple example to demonstrate how to handle and broadcast messages to several WebSocket connections.

    But keep in mind that, as everything is handled in memory, in a single list, it will only work while the process is running, and will only work with a single process.

    connections.

 *  Fix: Immediately update the connection's flow control window instead of waiting for the
    receiving stream to process it.

    This change may increase OkHttp's memory use for applications that make many concurrent HTTP
    calls and that can receive data faster than they can process it. Previously, OkHttp limited

 * your HTTP calls. This is because each client holds its own connection pool and thread pools.
 * Reusing connections and threads reduces latency and saves memory. Conversely, creating a client
 * for each request wastes resources on idle pools.
 *
 * Use `new OkHttpClient()` to create a shared instance with the default settings:
 *
 * ```java
 * // The singleton HTTP client.