}

	bp.Put(b)

	// Check the size of the pool.
	if uint64(len(bp.c)) != size {
		t.Fatalf("bytepool size invalid: got %v want %v", len(bp.c), size)
	}

	// lets drain the buf channel first before we validate invalid buffers.
	for range size {
		bp.Get() // discard
	}

	// Try putting some invalid buffers into pool

	return dstRec, nil
}

// Close - closes underlying reader.
func (r *Reader) Close() error {
	// Close the input.
	err := r.readCloser.Close()
	for range r.valueCh {
		// Drain values so we don't leak a goroutine.
		// Since we have closed the input, it should fail rather quickly.
	}
	return err
}

// NewReader - creates new JSON reader using readCloser.

func (s *Stream) Results(next func(b []byte) error) (err error) {
	done := false
	defer func() {
		if s.cancel != nil {
			s.cancel(err)
		}

		if !done {
			// Drain channel.
			for range s.responses {
			}
		}
	}()
	doneCh := s.ctx.Done()
	for {
		select {
		case <-doneCh:
			if err := context.Cause(s.ctx); !errors.Is(err, errStreamEOF) {
				return err
			}

	h          hash.Hash
	shardSize  int64
	hashBytes  []byte
}

func (b *streamingBitrotReader) Close() error {
	if b.rc == nil {
		return nil
	}
	if closer, ok := b.rc.(io.Closer); ok {
		// drain the body for connection reuse at network layer.
		xhttp.DrainBody(io.NopCloser(b.rc))
		return closer.Close()
	}
	return nil
}

func (b *streamingBitrotReader) ReadAt(buf []byte, offset int64) (int, error) {

	<-serverSent

	// Now do 100 other requests to ensure that the server doesn't block.
	for range 100 {
		_, err := remoteConn.Request(ctx, handlerTest2, []byte(testPayload))
		errFatal(err)
	}
	// Drain responses
	got := 0
	for resp := range st.responses {
		// t.Log("got response", resp)
		errFatal(resp.Err)
		if resp.Msg[0] != byte(got) {
			t.Error("expected response", got, "got", resp.Msg[0])
		}

# Tests

tf_cuda_library(
    name = "c_test_util",
    testonly = 1,
    srcs = ["c_test_util.cc"],
    hdrs = ["c_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",

    * If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment: similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:

* Kullandığın geliştirme araçları ile iyi çalışır **<abbr title="Integrated Development Environment, kod editörüne benzer">IDE</abbr>/<abbr title="Code errorlarınızı inceleyen program">linter</abbr>/brain**:
    * Pydantic'in veri yapıları aslında sadece senin tanımladığın classlar; Bu yüzden doğrulanmış dataların ile otomatik tamamlama, linting ve mypy'ı kullanarak sorunsuz bir şekilde çalışabilirsin

* GCE: Prefer preconfigured node tags for firewalls, if available ([#25148](https://github.com/kubernetes/kubernetes/pull/25148), [@a-robinson](https://github.com/a-robinson))
* Drain pods created from ReplicaSets in 'kubectl drain' ([#23689](https://github.com/kubernetes/kubernetes/pull/23689), [@maclof](https://github.com/maclof))

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)