enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world...

 *   <li>It doesn't provide much control of threading.
 *   <li>It doesn't offer much monitoring.
 *   <li>It doesn't propagate exceptions, so apps don't have a way to react to them.
 *   <li>It doesn't interoperate well with RxJava, coroutines, and other more commonly used
 *       alternatives.
 *   <li>It imposes requirements on the lifecycle of its subscribers. For example, if an event

		for _, mf := range mfs {
			if err := enc.Encode(mf); err != nil {
				// client may disconnect for any reasons
				// we do not have to log this.
				return
			}
		}
		if closer, ok := enc.(expfmt.Closer); ok {
			closer.Close()
		}
	})
}

// NoAuthMiddleware no auth middle ware.
func NoAuthMiddleware(h http.Handler) http.Handler {
	return h
}

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      where such license applies only to those patent claims licensable
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      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
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