# 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

  @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

import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;

/**
 * Cache entry for directory contents with thread-safe operations
 */
public class DirectoryCacheEntry {

    private final String directoryPath;
    private final Smb2LeaseKey leaseKey;
    private final long createTime;

UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### 用另一个模型的内容生成 Pydantic 模型 { #a-pydantic-model-from-the-contents-of-another }

上例中 ，从 `user_in.model_dump()` 中得到了 `user_dict`，下面的代码：

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

等效于：

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

import java.io.Serializable;
import java.util.Map;
import org.jspecify.annotations.NonNull;
import org.jspecify.annotations.Nullable;

/**
 * A {@link ClassToInstanceMap} whose contents will never change, with many other important
 * properties detailed at {@link ImmutableCollection}.
 *
 * @author Kevin Bourrillion
 * @since 2.0
 */
@Immutable(containerOf = "B")
@GwtIncompatible

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

#### Bir Pydantic Modelinden Diğerinin İçeriğiyle Pydantic Model Oluşturmak { #a-pydantic-model-from-the-contents-of-another }

Yukarıdaki örnekte `user_dict`'i `user_in.model_dump()` ile elde ettiğimiz için, şu kod:

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

şuna eşdeğerdir:

        fail(
            "contents were not equal and in the same order: "
                + "expected = "
                + expected
                + ", actual = "
                + actual);
      }
    }

    if (expectedIter.hasNext() || actualIter.hasNext()) {
      // actual either had too few or too many elements
      fail(
          "contents were not equal and in the same order: "

import com.google.common.escape.Escaper;
import com.google.common.escape.Escapers;

/**
 * {@code Escaper} instances suitable for strings to be included in XML attribute values and
 * elements' text contents. When possible, avoid manual escaping by using templating systems and
 * high-level APIs that provide autoescaping. For example, consider <a
 * href="http://www.xom.nu/">XOM</a>.
 *

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

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

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

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

будет равнозначен такому:

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

#### Um modelo Pydantic a partir do conteúdo de outro { #a-pydantic-model-from-the-contents-of-another }

Como no exemplo acima, obtivemos o `user_dict` a partir do `user_in.model_dump()`, este código:

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

seria equivalente a: