assertContentType("application/xml", "extractor/test.mm", "hoge.mm");

        assertContentType("message/rfc822", "extractor/eml/sample1.eml", "sample1.eml");

        assertContentType("application/octet-stream", null, "hoge");

        assertContentType("application/vnd.ms-powerpoint", "extractor/msoffice/test.ppt", "h&oge.ppt");

		Short: "Retrieves the synchronization status of each Envoy in the mesh",
		Long: `
Retrieves last sent and last acknowledged xDS sync from Istiod to each Envoy in the mesh
`,
		Example: `  # Retrieve sync status for all Envoys in a mesh
  istioctl proxy-status

  # Retrieve sync status for Envoys in a specific namespace
  istioctl proxy-status --namespace foo

 * initiated via other mechanisms do not impact the correctness of its methods. For example, if the
 * services are started by some mechanism besides {@link #startAsync}, the listeners will be invoked
 * when appropriate and {@link #awaitHealthy} will still work as expected.
 *
 * <p>Here is a simple example of how to use a {@code ServiceManager} to start a server.
 *
 * <pre>{@code
 * class Server {

 * initiated via other mechanisms do not impact the correctness of its methods. For example, if the
 * services are started by some mechanism besides {@link #startAsync}, the listeners will be invoked
 * when appropriate and {@link #awaitHealthy} will still work as expected.
 *
 * <p>Here is a simple example of how to use a {@code ServiceManager} to start a server.
 *
 * <pre>{@code
 * class Server {

to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

        assertNotNull(builder);

        DefaultModelBuildingRequest request = new DefaultModelBuildingRequest();
        request.setProcessPlugins(true);
        request.setPomFile(getPom("simple"));

        ModelBuildingResult result = builder.build(request);
        assertNotNull(result);
        assertNotNull(result.getEffectiveModel());

# Debugging

You can connect the debugger in your editor, for example with Visual Studio Code or PyCharm.

## Call `uvicorn`

In your FastAPI application, import and run `uvicorn` directly:

```Python hl_lines="1  15"
{!../../docs_src/debugging/tutorial001.py!}
```

### About `__name__ == "__main__"`

The main purpose of the `__name__ == "__main__"` is to have some code that is executed when your file is called with:

    private ReserializedCollectionGenerator(OneSizeTestContainerGenerator<Collection<E>, E> gen) {
      this.gen = gen;
    }

    @Override
    public SampleElements<E> samples() {
      return gen.samples();
    }

    @Override
    public Collection<E> create(Object... elements) {
      return SerializableTester.reserialize(gen.create(elements));
    }

    @Override

namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}

// For range of 4 elements, Intel manual uses "+3" notation, for example:
//
//	VP4DPWSSDS zmm1{k1}{z}, zmm2+3, m128
//
// Given asm line:
//
//	VP4DPWSSDS Z5, [Z10-Z13], (AX)
//
// zmm2 is Z10, and Z13 is the only valid value for it (Z10+3).
// Only simple ranges are accepted, like [Z0-Z3].
//
// The opening bracket has been consumed.