In the previous chapter the security system (which is based on the dependency injection system) was giving the *path operation function* a `token` as a `str`:

{* ../../docs_src/security/tutorial001_an_py39.py hl[12] *}

But that is still not that useful.

Let's make it give us the current user.

## Create a user model { #create-a-user-model }

First, let's create a Pydantic user model.

        // Read some data
        assertEquals('T', stream.read());

        // Close should be ignored
        stream.close();

        // Should still be able to read
        assertEquals('e', stream.read());
        assertEquals('s', stream.read());
        assertEquals('t', stream.read());
    }

    public void test_read_delegatesToUnderlying() throws IOException {

        // Even with batching disabled, getBatchLimit should return a value
        int limit = config.getBatchLimit("TreeConnectAndX.Transaction");
        assertTrue(limit >= 0, "Batch limit should still return a value even when batching is disabled");
    }

    /**
     * Test configuration when Unicode is disabled
     */
    @Test
    @DisplayName("Batch limit configuration with Unicode disabled")

   *
   * @deprecated Prefer {@linkplain ExecutionError(Error)} a constructor that accepts a cause: Users
   *     of this class typically expect for instances to have a non-null cause. At the moment, you
   *     can <i>usually</i> still preserve behavior by passing an explicit {@code null} cause. Note,
   *     however, that passing an explicit {@code null} cause prevents anyone from calling {@link

        assertSame(b, reused, "Buffer should be reused from cache");
        // And it should still be zeroed
        for (byte value : reused) {
            assertEquals(0, value, "Reused buffer must be zeroed");
        }
    }

    // Edge: cache size 0 means nothing is cached; release still zeroes argument
    @Test
    @DisplayName("Cache size 0: release zeroes but does not cache; getBuffer allocates new")

# DevInfra CI Directory

> **Warning** This folder is still under construction. It is part of an ongoing
> effort to improve the structure of CI and build related files within the
> TensorFlow repo. This warning will be removed when the contents of this
> directory are stable and appropriate documentation around its usage is in
> place.

Maintainer: TensorFlow DevInfra

Issue Reporting: File an issue against this repo and tag

But this example is still valid and it shows how to interact with the internal components.

///

We can also use this same approach to access the request body in an exception handler.

       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
   

The next sections are **not necessarily "advanced"**.

And it's possible that for your use case, the solution is in one of them.

///

## Read the Tutorial first { #read-the-tutorial-first }

You could still use most of the features in **FastAPI** with the knowledge from the main [Tutorial - User Guide](../tutorial/index.md){.internal-link target=_blank}.

It had the best performance benchmarks at the time (only surpassed by Starlette).

At first, it didn't have an automatic API documentation web UI, but I knew I could add Swagger UI to it.

It had a dependency injection system. It required pre-registration of components, as other tools discussed above. But still, it was a great feature.