#!/usr/bin/env bash

echo "script failed" >resiliency-verify-healing.log # assume initial state

# Extract arguments from json object ...
FILE=$(echo $1 | jq -r '.args.file')
DIR=$(echo $1 | jq -r '.args.dir')
DEEP=$(echo $1 | jq -r '.args.deep')
WANT=$(echo $1 | jq 'del(.args)') # ... and remove args from wanted result

ALIAS_NAME=myminio
BUCKET="test-bucket"

* back and forth: de un lado a otro
* I/O (as in "input and output"): I/O (do not translate to "E/S")
* Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático")
* Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo")
* callback hell: callback hell (do not translate to "infierno de callbacks")
* tip: Consejo (do not translate to "tip")

    /**
     * Path or URL to be crawled by this configuration.
     */
    @Required
    @Size(max = 1000)
    public String crawlingConfigPath;

    /**
     * Maximum depth for crawling (how many levels deep to follow links).
     */
    @Min(value = 0)
    @Max(value = 2147483647)
    @ValidateTypeFailure
    public Integer depth;

    /**
     * Maximum number of pages/documents to access during crawling.
     */

<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">🔢 (⚖️ 🔢) 💪 🐍</a> ↩️ ⏬ 3️⃣.4️⃣.

///

/// tip

🚥 👆 💭, "📊", "🎓", &amp; "🍏" 📛 🎰 🏫 <abbr title="Technically, Deep Learning model architectures">🏷</abbr>.

///

### 📣 *➡ 🔢*

⤴️ ✍ *➡ 🔢* ⏮️ 🆎 ✍ ⚙️ 🔢 🎓 👆 ✍ (`ModelName`):

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

### ✅ 🩺

///

/// tip | Tips

"AlxexNet", "ResNet", dan "LeNet" adalah nama <abbr title="Secara teknis, arsitektur model Deep Learning">model</abbr> *Machine Learning*.

///

### Mendeklarasikan *parameter path*

Kemudian buat *parameter path* dengan tipe anotasi menggunakan *class* enum dari (`ModelName`)

        assertFalse(a.equals("not a blob"), "equals(other type) should be false");
    }

    // Ensures clone() returns a deep copy; mutations are independent across instances
    @Test
    @DisplayName("clone: returns deep copy and independent state")
    void clone_returnsDeepCopy() {
        // Arrange
        byte[] data = new byte[] { 10, 20, 30 };

		Recursive: true,                  // recursively heal all objects at 'prefix'
		Remove:    true,                  // remove content that has lost quorum and not recoverable
		ScanMode:  madmin.HealNormalScan, // by default do not do 'deep' scanning
	}

	start, _, err := madmClnt.Heal(context.Background(), "healing-rewrite-bucket", "", opts, "", false, false)
	if err != nil {
		log.Fatalln(err)
	}
	fmt.Println("Healstart sequence ===")

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

///

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

### Declarar un *path parameter*

Luego crea un *path parameter* con una anotación de tipo usando la clase enum que creaste (`ModelName`):

///

/// tip | Dica



///

	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):