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.

Like `item.model_dump(exclude_unset=True)`.

!!! info
    In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

    The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

# TensorFlow continuous integration

> **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

    public BuildResumptionData(final List<String> remainingProjects) {
        this.remainingProjects = remainingProjects;
    }

    /**
     * Returns the projects that still need to be built when resuming.
     * @return A list containing the group and artifact id of the projects.
     */
    public List<String> getRemainingProjects() {
        return this.remainingProjects;
    }

 *       cancel} on the {@code Future}). So beware: <i>Even if you cancel every preceding {@code
 *       Future} returned by this class, the next task may still have to wait.</i>.
 *   <li>Once an {@code AsyncCallable} returns a {@code Future}, this class considers that task to
 *       be "done" as soon as <i>that</i> {@code Future} completes in any way. Notably, a {@code

    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.

All we need to do is handle the request inside a `try`/`except` block:

	}
	if len(keys) != int(limit) {
		t.Fatalf("Expected length of the batch keys to be %v but got %v", limit, len(items))
	}
	batchLen = batch.Len()
	if batchLen != 0 {
		t.Fatalf("expected batch to be empty but still left with %d items", batchLen)
	}
	// Add duplicate entries
	for i := 0; i < 10; i++ {
		if err := batch.Add(99, 99); err != nil {
			t.Fatalf("failed to add duplicate item %v to batch after Get; %v", i, err)
		}
	}

       * 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).
   

 *     {@code MapMaker}, it was added to {@code CacheBuilder}, which throws {@code
 *     ExecutionException}, {@code UncheckedExecutionException}, and {@code ExecutionError}. Any
 *     code that is still catching {@code ComputationException} may need to be updated to catch some
 *     of those types instead. (Note that this type, though deprecated, is not planned to be removed
 *     from Guava.)
 */
@Deprecated
@GwtCompatible

   * documented as being for use with type parameters that have parametric nullness. But Converter's
   * type parameters do not. Still, we use it here so that we can suppress a warning at a smaller
   * level than the whole method but without performing a runtime null check. That way, we can still
   * pass null inputs to LegacyConverter, and it can violate the contract of Converter.
   *