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  1. docs/orchestration/kubernetes/README.md

    - Helm Chart: MinIO Helm Chart offers customizable and easy MinIO deployment with a single command. Refer [MinIO Helm Chart documentation](https://github.com/minio/minio/tree/master/helm/minio) for more details.
    
    ## Monitoring MinIO in Kubernetes
    
    Registered: Sun Sep 07 19:28:11 UTC 2025
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  2. docs/en/docs/project-generation.md

    # Full Stack FastAPI Template { #full-stack-fastapi-template }
    
    Templates, while typically come with a specific setup, are designed to be flexible and customizable. This allows you to modify and adapt them to your project's requirements, making them an excellent starting point. 🏁
    
    You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.
    
    Registered: Sun Sep 07 07:19:17 UTC 2025
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  3. README.md

    - **Adapter Pattern** - Logging adapters (`JclLoggerAdapter`, `JulLoggerAdapter`) for different logging frameworks
    - **Template Method** - Resource traversal utilities with customizable handlers
    - **Utility Classes** - All core functionality exposed through static utility methods for easy access
    
    ### Performance Optimizations
    Registered: Fri Sep 05 20:58:11 UTC 2025
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  4. docs/en/docs/alternatives.md

    They are, more or less, at opposite ends, complementing each other.
    
    Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.
    
    That's why, as said in the official website:
    
    > Requests is one of the most downloaded Python packages of all time
    
    The way you use it is very simple. For example, to do a `GET` request, you would write:
    
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  5. RELEASE.md

            deprecated and will be removed in a future release.
        *   Metrics update and collection logic in default `Model.train_step()` is
            now customizable via overriding `Model.compute_metrics()`.
        *   Losses computation logic in default `Model.train_step()` is now
            customizable via overriding `Model.compute_loss()`.
        *   `jit_compile` added to `Model.compile()` on an opt-in basis to compile
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