}
```

🔄, ⚫️ 🔜 🗜 📊 🆎, ✔, 📄, ♒️.

## 💗 💪 = & 🔢

↗️, 👆 💪 📣 🌖 🔢 🔢 🕐❔ 👆 💪, 🌖 🙆 💪 🔢.

, 🔢, ⭐ 💲 🔬 🔢 🔢, 👆 🚫 ✔️ 🎯 🚮 `Query`, 👆 💪:

```Python
q: Union[str, None] = None
```

⚖️ 🐍 3️⃣.1️⃣0️⃣ & 🔛:

```Python
q: str | None = None
```

🖼:

//// tab | 🐍 3️⃣.6️⃣ & 🔛

```Python hl_lines="27"

		ft := f.Type

		// In godefs mode, if this field is a C11
		// anonymous union then treat the first field in the
		// union as the field in the struct. This handles
		// cases like the glibc <sys/resource.h> file; see
		// issue 6677.
		if *godefs {
			if st, ok := f.Type.(*dwarf.StructType); ok && name == "" && st.Kind == "union" && len(st.Field) > 0 && !used[st.Field[0].Name] {
				name = st.Field[0].Name

affinity towards odd number erasure sets to provide for uniform distribution across nodes*. This is to ensure that same number of drives are pariticipating in any erasure set. For example if you have 2 nodes with 180 drives then GCD is 15 but this would lead to uneven distribution, one of the nodes would participate more drives. To avoid this the affinity is given towards nodes which leads to next best GCD factor of 12 which provides uniform distribution.

- In this algorithm, we also make...

 */
public class LoadingCacheSingleThreadBenchmark {
  @Param({"1000", "2000"})
  int maximumSize;

  @Param("5000")
  int distinctKeys;

  @Param("4")
  int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union
// type of eager and graph tensors. It is also the result of executing an
// operation.
typedef struct TF_AbstractTensor TF_AbstractTensor;

// A TF_AbstractOp is the metadata we need to execute an operation. E.g. this

		{rulesCase4, rules2Case4, expectedResultCase4},
		{rulesCase5, rules2Case5, expectedResultCase5},
	}

	for i, testCase := range testCases {
		result := testCase.rules.Union(testCase.rules2)

		if !reflect.DeepEqual(testCase.expectedResult, result) {
			t.Fatalf("test %v: result: expected: %v, got: %v", i+1, testCase.expectedResult, result)
		}
	}
}

  //
  // Element-level accessors
  //

  /**
   * Returns a live view of the nodes which have an incident edge in common with {@code node} in
   * this graph.
   *
   * <p>This is equal to the union of {@link #predecessors(Object)} and {@link #successors(Object)}.
   *
   * <p>If {@code node} is removed from the graph after this method is called, the {@code Set}

### ❌ 📨 🆎 ✍

✋️ 🕐❔ 👆 📨 🎏 ❌ 🎚 👈 🚫 ☑ Pydantic 🆎 (✅ 💽 🎚) & 👆 ✍ ⚫️ 💖 👈 🔢, FastAPI 🔜 🔄 ✍ Pydantic 📨 🏷 ⚪️➡️ 👈 🆎 ✍, & 🔜 ❌.

🎏 🔜 🔨 🚥 👆 ✔️ 🕳 💖 <abbr title='A union between multiple types means "any of these types".'>🇪🇺</abbr> 🖖 🎏 🆎 🌐❔ 1️⃣ ⚖️ 🌅 👫 🚫 ☑ Pydantic 🆎, 🖼 👉 🔜 ❌ 👶:

//// tab | 🐍 3️⃣.6️⃣ &amp; 🔛

```Python hl_lines="10"

a frente, quando você ler sobre como estruturar aplicações maiores ([Bigger Applications - Multiple Files](../../tutorial/bigger-applications.md){.internal-link target=_blank}), possivelmente com múltiplos arquivos, você irá aprender a declarar um único parâmetro `dependencies` para um conjunto de *operações de rota*....

Mas na maioria dos casos, você precisará executar essas etapas apenas **uma vez**.

Portanto, você vai querer ter um **processo único** para executar essas **etapas anteriores** antes de iniciar o aplicativo.