* }
 *
 * <p>Note that the behavior of {@link Range#isEmpty()} and {@link Range#isConnected(Range)} may not
 * be as expected on discrete ranges. See the Javadoc of those methods for details.
 *
 * <p>For a {@link Set} whose contents are specified by a {@link Range}, see {@link ContiguousSet}.
 *
 * <p>See the Guava User Guide article on <a href=
 * "https://github.com/google/guava/wiki/NewCollectionTypesExplained#rangeset">RangeSets</a>.
 *

    // Helper to build a minimal smb URL
    private static URL smbUrl(String spec) throws Exception {
        return new URL(null, spec, new Handler());
    }

    @Test
    @DisplayName("getConfig returns context config")
    void getConfig_returnsConfig() {
        SmbTreeConnection c = newConn();
        assertSame(config, c.getConfig());
    }

    @Test

# This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl
# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    steps:
    - name: Checkout code

Então podemos declarar `tags` como um conjunto de strings:

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

Com isso, mesmo que você receba uma requisição contendo dados duplicados, ela será convertida em um conjunto de itens exclusivos.

E sempre que você enviar esses dados como resposta, mesmo se a fonte tiver duplicatas, eles serão gerados como um conjunto de itens exclusivos.

  @Override
  @GwtIncompatible // NavigableSet
  ImmutableSortedSet<C> createDescendingSet() {
    return new DescendingImmutableSortedSet<>(this);
  }

  /** Returns a shorthand representation of the contents such as {@code "[1..100]"}. */
  @Override
  public String toString() {
    return range().toString();
  }

  /**
   * Not supported. {@code ContiguousSet} instances are constructed with {@link #create}. This

        assertNotNull(result, String.format("Should create information for type %d, class %d", infoType, infoClass));
    }

    @Test
    @DisplayName("Test readBytesWireFormat preserves buffer content")
    void testReadBytesWireFormatPreservesBuffer() throws Exception {
        response = new Smb2QueryInfoResponse(mockConfig, Smb2Constants.SMB2_0_INFO_FILE, FileInformation.FILE_INTERNAL_INFO);

            if (n > 0 && (type = url.substring(n + 1)) != null && type.length() > 1 && type.length() < 6) {
                resp.setContentType(mimeMap.getMimeType(type));
            }
            resp.setHeader("Content-Length", file.length() + "");
            resp.setHeader("Accept-Ranges", "Bytes");

            while ((n = in.read(buf)) != -1) {
                out.write(buf, 0, n);
            }
        }

./mc ilm add myminio/bucket2 --transition-days 0 --transition-tier TIER1 --transition-days 0

## mirror some content to bucket2 and capture versions tiered
./mc mirror internal myminio/bucket2/ --quiet >/dev/null
./mc ls -r myminio/bucket2/ >bucket2_ns.txt
./mc ls -r --versions myminio/bucket2/ >bucket2_ns_versions.txt

sleep 30

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Ein Pydantic-Modell aus dem Inhalt eines anderen { #a-pydantic-model-from-the-contents-of-another }

Da wir im obigen Beispiel `user_dict` von `user_in.model_dump()` bekommen haben, wäre dieser Code:

```Python
user_dict = user_in.model_dump()
UserInDB(**user_dict)
```

gleichwertig zu:

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Pydantic-модель из содержимого другой модели { #a-pydantic-model-from-the-contents-of-another }

Как в примере выше мы получили `user_dict` из `user_in.dict()`, этот код:

```Python
user_dict = user_in.dict()
UserInDB(**user_dict)
```

будет равнозначен такому:

```Python