On top of that, an async framework could run some sync code in a threadpool (using `asyncio.run_in_executor`), but belonging to the same request.

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced

### Create the `Settings` object

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

=== "Pydantic v2"

   * "inherited" from superclasses of the same package.
   */
  public void testAllPublicStaticMethods(Class<?> c) {
    testStaticMethods(c, Visibility.PUBLIC);
  }

  /**
   * Runs {@link #testMethod} on every instance method of the class of {@code instance} with at
   * least {@code minimalVisibility}, including those inherited from superclasses of the same
   * package.
   */

# Envoy binary variables
ISTIO_ENVOY_BASE_URL="${ISTIO_ENVOY_BASE_URL:-https://storage.googleapis.com/istio-build/proxy}"

# If we are not using the default, assume its private and we need to authenticate
if [[ "${ISTIO_ENVOY_BASE_URL}" != "https://storage.googleapis.com/istio-build/proxy" ]]; then
  AUTH_HEADER="Authorization: Bearer $(gcloud auth print-access-token)"
  export AUTH_HEADER

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