2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```
(You can paste links or attach files by dragging & dropping them below)

        extraSteps = extraBuildSteps,
        preSteps = preBuildSteps
    )

    failureConditions {
        // JavaExecDebugIntegrationTest.debug session fails without debugger might cause JVM crash
        // Some soak tests produce OOM exceptions
        // There are also random worker crashes for some tests.
        // We have test-retry to handle the crash in tests
        javaCrash = false

Der `RequestValidationError` enthält den empfangenen `body` mit den ungültigen Daten.

Sie könnten diesen verwenden, während Sie Ihre Anwendung entwickeln, um den Body zu loggen und zu debuggen, ihn zum Benutzer zurückzugeben, usw.

```Python hl_lines="14"
{!../../../docs_src/handling_errors/tutorial005.py!}
```

Jetzt versuchen Sie, einen ungültigen Artikel zu senden:

```JSON
{

#### Debug builds

When [building Tensorflow](https://www.tensorflow.org/install/source), passing
`--config=dbg` to Bazel will build with debugging information and without
optimizations, allowing you to use GDB or other debuggers to debug C++ code. For
example, you can build the pip package with debugging information by running:

```bash
bazel build --config=dbg //tensorflow/tools/pip_package:build_pip_package
```

vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference

## Exécutez votre code avec votre <abbr title="En anglais: debugger">débogueur</abbr>

Parce que vous exécutez le serveur Uvicorn directement depuis votre code, vous pouvez appeler votre programme Python (votre application FastAPI) directement depuis le <abbr title="En anglais: debugger">débogueur</abbr>.

---

Par exemple, dans Visual Studio Code, vous pouvez :

Daten **nicht übereinstimmen**.

Sie würden also sehr früh im Entwicklungszyklus **viele Fehler erkennen**, anstatt darauf warten zu müssen, dass die Fehler Ihren Endbenutzern in der Produktion angezeigt werden, und dann zu versuchen, zu debuggen, wo das Problem liegt. ✨...

## Run your code with your debugger

Because you are running the Uvicorn server directly from your code, you can call your Python program (your FastAPI application) directly from the debugger.

---

For example, in Visual Studio Code, you can:

* Go to the "Debug" panel.
* "Add configuration...".
* Select "Python"
* Run the debugger with the option "`Python: Current File (Integrated Terminal)`".

- [`KT-55072`](https://youtrack.jetbrains.com/issue/KT-55072) K2: False positive "suspension point is inside a critical section"
- [`KT-58778`](https://youtrack.jetbrains.com/issue/KT-58778) JVM IR inline: add fake variables for debugger
- [`KT-59404`](https://youtrack.jetbrains.com/issue/KT-59404) K2: Missing EXPECT_TYPE_IN_WHEN_WITHOUT_ELSE

    Lite, supported by new `tf.contrib.quantize` package.
*   Easily customize gradient computation with `tf.custom_gradient`.
*   [TensorBoard Debugger Plugin](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md),
    the graphical user interface (GUI) of TensorFlow Debugger (tfdbg), is now in
    alpha.
*   Experimental support for reading a sqlite database as a `Dataset` with new