logger.info(content);
        assertTrue(content.contains("テスト"));
    }

    public void test_getText_null() {
        try {
            textExtractor.getText(null, null);
            fail();
        } catch (final CrawlerSystemException e) {
            // NOP
        }
    }

/// tip | Dica

Integração com outros provedores de autenticação/autorização como Google, Facebook, X (Twitter), GitHub, etc. é bem possível e relativamente fácil.

O problema mais complexo é criar um provedor de autenticação/autorização como eles, mas o FastAPI dá a você ferramentas para fazer isso facilmente, enquanto faz o trabalho pesado para você.

///

venv_dir=$(mktemp -d)
if [[ $(uname -s) != MSYS_NT* ]]; then
  "python${TFCI_PYTHON_VERSION}" -m venv "$venv_dir"
  python="$venv_dir/bin/python3"
else
  # When using the Linux-like path, venv creation quietly fails, which is
  # why it's converted here.
  venv_dir=$(cygpath -m $venv_dir)
  "/c/python${TFCI_PYTHON_VERSION}/python.exe" -m venv "$venv_dir"
  python="$venv_dir/Scripts/python.exe"
fi

// Back returns the last element of list l or nil if the list is empty.
func (l *LruList[K, V]) Back() *Entry[K, V] {
	if l.len == 0 {
		return nil
	}
	return l.root.prev
}

// lazyInit lazily initializes a zero List Value.
func (l *LruList[K, V]) lazyInit() {
	if l.root.next == nil {
		l.Init()
	}
}

// insert inserts e after at, increments l.len, and returns e.

    }

    @Override
    public Artifact find(Artifact artifact) {
        File artifactFile = new File(localRepository.getBasedir(), pathOf(artifact));

        // We need to set the file here or the resolver will fail with an NPE, not fully equipped to deal
        // with multiple local repository implementations yet.
        artifact.setFile(artifactFile);

        return artifact;
    }

    @Override

* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    * You can have deeply **nested JSON** objects and have them all validated and annotated.
* **Extensible**:

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |

package gorm

import (
	"fmt"
	"reflect"
	"strings"

	"gorm.io/gorm/clause"
	"gorm.io/gorm/schema"
	"gorm.io/gorm/utils"
)

// Association Mode contains some helper methods to handle relationship things easily.
type Association struct {
	DB           *DB
	Relationship *schema.Relationship
	Unscope      bool
	Error        error
}

func (db *DB) Association(column string) *Association {

// TF_AttrMetadata.list_size and `storage` would be at least
// TF_AttrMetadata.total_size, obtained from TF_OperationGetAttrMetadata(oper,
// attr_name).
//
// Fails if storage_size is too small to hold the requested number of strings.
TF_CAPI_EXPORT extern void TF_OperationGetAttrStringList(
    TF_Operation* oper, const char* attr_name, void** values, size_t* lengths,

func (sys *NotificationSys) VerifyBinary(ctx context.Context, u *url.URL, sha256Sum []byte, releaseInfo string, bin []byte) []NotificationPeerErr {
	// FIXME: network calls made in this manner such as one goroutine per node,
	// can easily eat into the internode bandwidth. This function would be mostly
	// TX saturating, however there are situations where a RX might also saturate.
	// To avoid these problems we must split the work at scale. With 1000 node