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
			}

	// ReplicateQueued - replication being queued trail
	ReplicateQueued = "replicate:queue"

	// ReplicateExisting - audit trail for existing objects replication
	ReplicateExisting = "replicate:existing"
	// ReplicateExistingDelete - audit trail for delete replication triggered for existing delete markers
	ReplicateExistingDelete = "replicate:existing:delete"

	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))

> http://host1/export5
> http://host2/export5
> http://host1/export6
> http://host2/export6
> http://host1/export7
> http://host2/export7
> http://host1/export8
> http://host2/export8
```

*A noticeable trait of this expansion is that it chooses unique hosts such the setup provides maximum protection and availability.*