## 1. Prerequisites

* Familiarity with [Docker Compose](https://docs.docker.com/compose/overview/).

// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package grid

import (
	"context"
	"errors"
)

// A Stream is a two-way stream.
// All responses *must* be read by the caller.
// If the call is canceled through the context,
// the appropriate error will be returned.
type Stream struct {
	// responses from the remote server.

      }
    }
  }

  /**
   * Implementation of the InvertedMinMaxPriorityQueue which forwards all calls to a
   * MinMaxPriorityQueue, except poll, which is forwarded to pollMax. That way we can benchmark
   * pollMax using the same code that benchmarks poll.
   */
  static final class InvertedMinMaxPriorityQueue<T> extends ForwardingQueue<T> {
    MinMaxPriorityQueue<T> mmHeap;

 * [HTTP Public Key Pinning (HPKP)][rfc_7469] or SHA-1 base64 hashes as in Chromium's
 * [static certificates][static_certificates].
 *
 * ## Setting up Certificate Pinning
 *
 * The easiest way to pin a host is turn on pinning with a broken configuration and read the
 * expected configuration when the connection fails. Be sure to do this on a trusted network, and

func newStreamHandler[Payload, Req, Resp RoundTripper](h HandlerID) *StreamTypeHandler[Payload, Req, Resp] {
	return &StreamTypeHandler[Payload, Req, Resp]{id: h, InCapacity: 1, OutCapacity: 1}
}

// Register a handler for two-way streaming with payload, input stream and output stream.
// An optional subroute can be given. Multiple entries are joined with '/'.

  //
  // TODO: Determine how to detect ContentType and ContentEncoding of 'Raw' data.
  optional bytes raw = 1;
}

// TypeMeta is shared by all top level objects. The proper way to use it is to inline it in your type,
// like this:
//
// 	type MyAwesomeAPIObject struct {
// 	     runtime.TypeMeta    `json:",inline"`
// 	     ... // other fields
// 	}
//

/**
 * Implementation detail for the internal structure of {@link Range} instances. Represents a unique
 * way of "cutting" a "number line" (actually of instances of type {@code C}, not necessarily
 * "numbers") into two sections; this can be done below a certain value, above a certain value,
 * below all values or above all values. With this object defined in this way, an interval can
 * always be represented by a pair of {@code Cut} instances.
 *

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

 *    crashes the application on Android and prints a stacktrace on the JVM. (Crash reporting
 *    libraries may customize this behavior.)
 *
 * A good way to signal a failure is with a synthetic HTTP response:
 *
 * ```kotlin
 *   @Throws(IOException::class)
 *   override fun intercept(chain: Interceptor.Chain): Response {
 *     if (myConfig.isInvalid()) {

/// tip

Prefer to use the `Annotated` version if possible.

///

```Python hl_lines="17"
{!> ../../docs_src/dependencies/tutorial006.py!}
```

////

These dependencies will be executed/solved the same way as normal dependencies. But their value (if they return any) won't be passed to your *path operation function*.

/// tip

Some editors check for unused function parameters, and show them as errors.