*   XLA reduction emitter is deterministic when the environment variable
        `TF_DETERMINISTIC_OPS` is set to "true" or "1". This extends
        deterministic `tf.nn.bias_add` back-prop functionality (and therefore
        also deterministic back-prop of bias-addition in Keras layers) to
        include when XLA JIT compilation is enabled.
    *   Fix problem, when running on a CUDA GPU and when either environment

Also allowing to have multiple alternative security schemes that are then checked in a single dependency instead of each one verifying and returning the error to the client automatically when not satisfied. PR [#134](https://github.com/tiangolo/fastapi/pull/134).

* Update [SQL Tutorial](https://fastapi.tiangolo.com/tutorial/sql-databases/#create-a-middleware-to-handle-sessions) to close database sessions even when there are exceptions. PR [#89](https://github.com/tiangolo/fastapi/pull/89)...

        def annotations = []
        jApiType.annotations >> annotations

        when:
        annotations.clear()

        then:
        rule.maybeViolation(jApiType).humanExplanation =~ 'Is not annotated with @Incubating'

        when:
        annotations.add(incubatingAnnotation)

        then:
        rule.maybeViolation(jApiType) == null

        where:
        apiElement    | jApiTypeName

    def canAddMetaData() {
        TestDomainObject value = new TestDomainObject('a')

        when:
        repository.put('class', value)

        then:
        repository.find('class') == value
        repository.get('class') == value
    }

    def findReturnsNullForUnknownClass() {
        expect:

   * <p>If the dataset contains {@link Double#NaN} then the result is {@link Double#NaN}. If it
   * contains {@link Double#NEGATIVE_INFINITY} and not {@link Double#NaN} then the result is {@link
   * Double#NEGATIVE_INFINITY}. If it contains {@link Double#POSITIVE_INFINITY} and finite values
   * only then the result is the lowest finite value. If it contains {@link
   * Double#POSITIVE_INFINITY} only then the result is {@link Double#POSITIVE_INFINITY}.
   *

fi
set -x

# When cross-compiling with RBE, we need to copy the macOS sysroot to be
# inside the TensorFlow root directory. We then define them as a filegroup
# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.

And then, right after the `yield`, we unload the model. This code will be executed **after** the application **finishes handling requests**, right before the *shutdown*. This could, for example, release resources like memory or a GPU.

!!! tip
    The `shutdown` would happen when you are **stopping** the application.

  Find the control point for m2 using maven-wagon. At this point, inject
  a offline status parameter which is used when retrieving the specific Wagon.

  If <<<offline == true>>>:

  * If the wagon is not bound to "file://", then ignore the request and print
    a debug message.

  * If the wagon is bound to "file://" then:

    Retrieve the file or base-url file to be "downloaded".

name: str
```

Keep these in mind, so you don't get confused when using `=` and `:` with them.

### Create Pydantic *models* / schemas for reading / returning

Now create Pydantic *models* (schemas) that will be used when reading data, when returning it from the API.

For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.

     * If the given {@code paths} argument contains no element, then this method returns an empty string.
     *
     * <p><b>Examples:</b>
     * If {@code paths} is a list containing two elements, {@code path1} and {@code path2}, then:
     * </p>
     * <ul>
     *   <li>If this type is {@link JavaPathType#MODULES}, then this method returns