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  1. docs/en/docs/async.md

    ### Burger Conclusion
    
    In this scenario of "fast food burgers with your crush", as there is a lot of waiting 🕙, it makes a lot more sense to have a concurrent system ⏸🔀⏯.
    
    This is the case for most of the web applications.
    
    Many, many users, but your server is waiting 🕙 for their not-so-good connection to send their requests.
    
    And then waiting 🕙 again for the responses to come back.
    
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  2. docs/en/docs/advanced/index.md

    And it shows their true commitment to FastAPI and its **community** (you), as they not only want to provide you a **good learning experience** but also want to make sure you have a **good and healthy framework**, FastAPI. 🙇
    
    You might want to try their courses:
    
    * <a href="https://training.talkpython.fm/fastapi-courses" class="external-link" target="_blank">Talk Python Training</a>
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  3. CONTRIBUTING.md

        rare occasion we may reject it.
    
    **3. Review**
    
    -   For a valid PR, reviewer (person familiar with the code/functionality)
        checks if the PR looks good or needs additional changes.
    -   If all looks good, the reviewer will approve the PR.
    -   If a change is needed, the contributor is requested to make the suggested
        change.
    -   You make the change and submit it for the review again.
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  4. CREDITS

    github.com/ncw/directio
    https://github.com/ncw/directio
    ----------------------------------------------------------------
    Copyright (C) 2012 by Nick Craig-Wood http://www.craig-wood.com/nick/
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
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  5. okhttp/src/test/java/okhttp3/internal/http2/HttpOverHttp2Test.kt

            .build(),
        )
        server.enqueue(
          MockResponse(body = "abc"),
        )
        // Enqueue an additional response that show if we burnt a good prior response.
        server.enqueue(
          MockResponse(body = "XXX"),
        )
        val call1 = client.newCall(Request(server.url("/")))
        val response1 = call1.execute()
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  6. docs/en/docs/help-fastapi.md

    ### Understand the problem
    
    * First, make sure you **understand the problem** that the pull request is trying to solve. It might have a longer discussion in a GitHub Discussion or issue.
    
    * There's also a good chance that the pull request is not actually needed because the problem can be solved in a **different way**. Then you can suggest or ask about that.
    
    ### Don't worry about style
    
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  7. docs/tr/docs/async.md

    ### Parallel Hamburgerler
    
    Şimdi bunların "Eşzamanlı Hamburger" değil, "Paralel Hamburger" olduğunu düşünelim.
    
    Hamburger 🍔 almak için 😍 aşkınla Paralel fast food'a gidiyorsun.
    
    Birden fazla kasiyer varken (varsayalım 8) sıraya girdiniz👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳 ve sıranız gelene kadar bekliyorsunuz.
    
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  8. CHANGELOG.md

        prefer the JDK’s precedence order.) This change may cause your HTTP calls to negotiate a
        different cipher suite than before! OkHttp's defaults cipher suites are selected for good
        security and performance.
    
     *  New: `ConnectionListener` publishes events for connects, disconnects, and use of pooled
        connections.
    
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  9. okhttp/src/main/kotlin/okhttp3/internal/http2/Http2Connection.kt

          val streamsCopy: Array<Http2Stream>
          ******@****.***ck {
            streamsCopy = streams.values.toTypedArray()
            isShutdown = true
          }
    
          // Fail all streams created after the last good stream ID.
          for (http2Stream in streamsCopy) {
            if (http2Stream.id > lastGoodStreamId && http2Stream.isLocallyInitiated) {
              http2Stream.receiveRstStream(REFUSED_STREAM)
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  10. RELEASE.md

        which is a NumPy-compatible API for writing TF programs. This module
        provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
        wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
        (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
        seamless in most cases. See
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