}

	fmt.Println("Bytes written:", n)
	fmt.Println("Buffer contents:", buf.String())
	// Output:
	// Bytes written: 41
	// Buffer contents: Hello, world!
	// This is a ReadFrom example.
}

// The simplest use of a Scanner, to read standard input as a set of lines.
func ExampleScanner_lines() {
	scanner := bufio.NewScanner(os.Stdin)
	for scanner.Scan() {
		fmt.Println(scanner.Text()) // Println will add back the final '\n'
	}

mc batch status myminio/ E24HH4nNMcgY5taynaPfxu
●∙∙
Objects:        28766
Versions:       28766
Throughput:     3.0 MiB/s
Transferred:    406 MiB
Elapsed:        2m14.227222868s
CurrObjName:    share/doc/xml-core/examples/foo.xmlcatalogs
```

### 'describe' the batch job yaml.
```
mc batch describe myminio/ E24HH4nNMcgY5taynaPfxu
replicate:
  apiVersion: v1
...

   *
   * <p>If all input data to this {@code TopKSelector} is in a single {@code Iterable}, prefer
   * {@link Ordering#leastOf(Iterable, int)}, which provides a simpler API for that use case.
   */
  public void offerAll(Iterable<? extends T> elements) {
    offerAll(elements.iterator());
  }

  /**

        return false;
      }
    }
    return true;
  }

  /**
   * Returns {@code true} if the bounds of {@code other} do not extend outside the bounds of this
   * range. Examples:
   *
   * <ul>
   *   <li>{@code [3..6]} encloses {@code [4..5]}
   *   <li>{@code (3..6)} encloses {@code (3..6)}
   *   <li>{@code [3..6]} encloses {@code [4..4)} (even though the latter is empty)

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

Mas você também pode explorar os benefícios do paralelismo e multiprocessamento (tendo múltiplos processadores rodando em paralelo) para trabalhos **limitados por CPU** como aqueles em sistemas de Aprendizado de Máquina.

Isso, somado ao simples fato que Python é a principal linguagem para **Data Science**, Aprendizado de Máquina e especialmente Deep Learning, faz do FastAPI uma ótima escolha para APIs web e aplicações com Data Science / Aprendizado de Máquina (entre muitas outras).

import java.util.ArrayList;
import java.util.List;
import org.jspecify.annotations.NullMarked;
import org.jspecify.annotations.Nullable;

/**
 * Tester for equals() and hashCode() methods of a class.
 *
 * <p>The simplest use case is:
 *
 * <pre>
 * new EqualsTester().addEqualityGroup(foo).testEquals();
 * </pre>
 *
 * <p>This tests {@code foo.equals(foo)}, {@code foo.equals(null)}, and a few other operations.
 *

## Simple HTTP Basic Auth { #simple-http-basic-auth }

* Import `HTTPBasic` and `HTTPBasicCredentials`.
* Create a "`security` scheme" using `HTTPBasic`.
* Use that `security` with a dependency in your *path operation*.
* It returns an object of type `HTTPBasicCredentials`:
    * It contains the `username` and `password` sent.

	// form http://localhost:8181/v1/data/httpapi/authz
	type opaResultAllow struct {
		Result struct {
			Allow bool `json:"allow"`
		} `json:"result"`
	}

	// Handle simpler OPA responses when OPA URL is of
	// form http://localhost:8181/v1/data/httpapi/authz/allow
	type opaResult struct {
		Result bool `json:"result"`
	}

	respBody := bytes.NewReader(opaRespBytes)

        * `authorizationCode`
    * But there is one specific "flow" that can be perfectly used for handling authentication in the same application directly:
        * `password`: some next chapters will cover examples of this.
* `openIdConnect`: has a way to define how to discover OAuth2 authentication data automatically.
    * This automatic discovery is what is defined in the OpenID Connect specification.


/// tip