* If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:

    }

    /**
     * @return the tid
     */
    public int getTid() {
        return this.tid;
    }

    /**
     * @return the tree_num (monotonically increasing counter to track reconnects)
     */
    public long getTreeNum() {
        return this.treeNum;
    }

    /**
     * {@inheritDoc}
     *
     * @see java.lang.Object#hashCode()
     */

import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.ValueSource;

/**
 * Test class for Smb3KeyDerivation.
 * Tests the SMB3 SP800-108 Counter Mode Key Derivation implementation.
 */
@DisplayName("Smb3KeyDerivation Tests")
class Smb3KeyDerivationTest {

    private byte[] sessionKey;
    private byte[] preauthIntegrity;

    @BeforeEach

* Vai bem com o/a seu/sua **<abbr title="Ambiente de Desenvolvimento Integrado, similar a um editor de código">IDE</abbr>/<abbr title="Um programa que confere erros de código">linter</abbr>/cérebro**:
    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, a auto completação, _linting_, _mypy_ e a sua intuição devem funcionar corretamente com seus dados validados.

        assertNull(crawlingConfigHelper.getCrawlingConfig("W123"));
    }

    public void test_store_counterIncrement() {
        final String sessionId = "testSession";

        // Test counter increment
        String sessionCountId1 = crawlingConfigHelper.store(sessionId, crawlingConfigHelper.getCrawlingConfig("W1"));
        assertEquals("testSession-1", sessionCountId1);

  if (input_tensors != nullptr) {
    // Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();

* **更簡單**：
    * 不需要學習新的 micro-language 來定義結構。
    * 如果你知道 Python 型別，你就知道如何使用 Pydantic。
* 和你的 **<abbr title="Integrated Development Environment，和程式碼編輯器類似">IDE</abbr>/<abbr title="一個檢查程式碼錯誤的工具">linter</abbr>/brain** 都能好好配合：
    * 因為 Pydantic 的資料結構其實就是你自己定義的類別實例，所以自動補齊、linting、mypy 以及你的直覺都能很好地在經過驗證的資料上發揮作用。
* 驗證**複雜結構**：
    * 使用 Pydantic 模型時，你可以把資料結構分層設計，並且用 Python 的 `List` 和 `Dict` 等型別來定義。

   * }
   *
   * <p>Note that if any input list is empty, the Cartesian product will also be empty. If no lists
   * at all are provided (an empty list), the resulting Cartesian product has one element, an empty
   * list (counter-intuitive, but mathematically consistent).
   *
   * <p><i>Performance notes:</i> while the cartesian product of lists of size {@code m, n, p} is a

import com.google.common.annotations.Beta;
import com.google.common.annotations.GwtIncompatible;
import com.google.common.annotations.J2ktIncompatible;
import com.google.common.base.Joiner;
import com.google.common.base.Optional;
import com.google.common.base.Predicate;
import com.google.common.base.Splitter;
import com.google.common.collect.ImmutableList;
import com.google.common.collect.ImmutableSet;

}

// Cap returns the capacity of the cache
func (c *LRU[K, V]) Cap() int {
	return c.size
}

// Entry is an LRU Entry
type Entry[K comparable, V any] struct {
	// Next and previous pointers in the doubly-linked list of elements.
	// To simplify the implementation, internally a list l is implemented
	// as a ring, such that &l.root is both the next element of the last