They are, more or less, at opposite ends, complementing each other.

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

That's why, as said in the official website:

> Requests is one of the most downloaded Python packages of all time

The way you use it is very simple. For example, to do a `GET` request, you would write:

```Python

# Each automation is independent of the others.  Every time one of the `on` conditions match for
# this PR, this automations will have its `if` checked to run.  In a way, the `on` conditions
# function as an implicit first `if` for every automation in the file.

# You can define multiple automations in a .cm file, but each automation name should be unique
# within the file.  We keep each automation (or very closely related group of automations) in

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats
Here are the affected tests:
EOF
    while read dep; do
      echo "For dependency $dep:"
      # For every missing dependency, find the tests which directly depend on
      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.

                out <- []byte("response")

                // Return the request for reuse
                grid.PutByteBuffer(req)
            }
        }
        // out is closed by the caller and should never be closed by the handler.
        return nil
    }

    err := manager.RegisterStreamingHandler(grid.HandlerDiskInfo, StreamHandler{
        Handle: handler,
        Subroute: "asubroute",

       - The compressed index.xml file contains a timestamp property which
       changes with every test execution, such that running the test
       actually changes the test classpath itself. This means that it
       can"t benefit from incremental build acceleration, because on every
       execution it sees that the classpath has changed, and so to be
       safe, it needs to re-run.

  // level:
  //
  //             ACV(l) = ceil( SCL * ACS(l) / ( sum[priority levels k] ACS(k) ) )
  //
  // bigger numbers of ACS mean more reserved concurrent requests (at the
  // expense of every other PL).
  // This field has a default value of 30.
  // +optional
  optional int32 assuredConcurrencyShares = 1;

  // `limitResponse` indicates what to do with requests that can not be executed right now

  //
  // NominalCL(i)  = ceil( ServerCL * NCS(i) / sum_ncs )
  // sum_ncs = sum[limited priority level k] NCS(k)
  //
  // Bigger numbers mean a larger nominal concurrency limit,
  // at the expense of every other Limited priority level.
  // This field has a default value of 30.
  // +optional
  optional int32 nominalConcurrencyShares = 1;

distribute the same sections as part of a whole which is a work based
on the Library, the distribution of the whole must be on the terms of
this License, whose permissions for other licensees extend to the
entire whole, and thus to each and every part regardless of who wrote
it.

Thus, it is not the intent of this section to claim rights or contest
your rights to work written entirely by you; rather, the intent is to

This is just a **quick tutorial / refresher** about Python type hints. It covers only the minimum necessary to use them with **FastAPI**... which is actually very little.

**FastAPI** is all based on these type hints, they give it many advantages and benefits.

But even if you never use **FastAPI**, you would benefit from learning a bit about them.

!!! note

seja executado pelo mesmo processo (por exemplo, você não precisa compartilhar memória, variáveis, etc), você pode se beneficiar do uso de outras ferramentas maiores, como <a href="http://www.celeryproject.org/" class="external-link" target="_blank"> Celery </a>.

Eles tendem a exigir configurações mais complexas, um gerenciador de fila de mensagens/tarefas, como RabbitMQ ou Redis, mas permitem que você execute tarefas em segundo plano em vários processos e, especialmente, em vários servidores....