* PostgreSQL
* MySQL
* SQLite
* Oracle
* Microsoft SQL Server, и т.д.

В этом примере мы будем использовать **SQLite**, потому что она использует один файл и имеет встроенную поддержку в Python. Так что вы можете скопировать этот пример и запустить его как есть.

* PostgreSQL
* MySQL
* SQLite
* Oracle
* Microsoft SQL Server, etc.

En este ejemplo, usaremos **SQLite**, porque utiliza un solo archivo y Python tiene soporte integrado. Así que puedes copiar este ejemplo y ejecutarlo tal cual.

* PostgreSQL
* MySQL
* SQLite
* Oracle
* Microsoft SQL Server, etc.

Dans cet exemple, nous utiliserons **SQLite**, car il utilise un seul fichier et Python a un support intégré. Ainsi, vous pouvez copier cet exemple et l'exécuter tel quel.

///

SQLModel은 SQLAlchemy를 기반으로 하므로, SQLAlchemy에서 **지원하는 모든 데이터베이스**를 손쉽게 사용할 수 있습니다(이것들은 SQLModel에서도 지원됩니다). 예를 들면:

* PostgreSQL
* MySQL
* SQLite
* Oracle
* Microsoft SQL Server 등.

이 예제에서는 **SQLite**를 사용합니다. SQLite는 단일 파일을 사용하고 Python에서 통합 지원하기 때문입니다. 따라서 이 예제를 그대로 복사하여 실행할 수 있습니다.

나중에 프로덕션 애플리케이션에서는 **PostgreSQL**과 같은 데이터베이스 서버를 사용하는 것이 좋습니다.

/// tip | 팁

		t.Fatalf("Found deleted column")
	}
}

func TestMigrateColumns(t *testing.T) {
	tidbSkip(t, "use another test case")

	sqlite := DB.Dialector.Name() == "sqlite"
	sqlserver := DB.Dialector.Name() == "sqlserver"

	type ColumnStruct struct {
		gorm.Model
		Name  string
		Age   int    `gorm:"default:18;comment:my age"`

       *   `tflite::Interpreter::signature_keys`
       *   `tflite::Interpreter::signature_inputs`
       *   `tflite::Interpreter::signature_outputs`
       *   `tflite::Interpreter::input_tensor_by_signature`
       *   `tflite::Interpreter::output_tensor_by_signature`

    *  Similarly, the following signature runner functions in the TF Lite C API are no longer considered experimental:

       *    `TfLiteInterpreterGetSignatureCount`

      Spliterator<? extends T> splitr = stream.spliterator();
      splitrsBuilder.add(splitr);
      characteristics &= splitr.characteristics();
      estimatedSize = LongMath.saturatedAdd(estimatedSize, splitr.estimateSize());
    }
    return StreamSupport.stream(
            CollectSpliterators.flatMap(
                splitrsBuilder.build().spliterator(),
                splitr -> (Spliterator<T>) splitr,

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```
(You can paste links or attach files by dragging & dropping them below)

      Spliterator<? extends T> splitr = stream.spliterator();
      splitrsBuilder.add(splitr);
      characteristics &= splitr.characteristics();
      estimatedSize = LongMath.saturatedAdd(estimatedSize, splitr.estimateSize());
    }
    return StreamSupport.stream(
            CollectSpliterators.flatMap(
                splitrsBuilder.build().spliterator(),
                splitr -> (Spliterator<T>) splitr,

	return genSplit(s, sep, len(sep), n)
}

// Split slices s into all subslices separated by sep and returns a slice of
// the subslices between those separators.
// If sep is empty, Split splits after each UTF-8 sequence.
// It is equivalent to SplitN with a count of -1.
//
// To split around the first instance of a separator, see [Cut].
func Split(s, sep []byte) [][]byte { return genSplit(s, sep, 0, -1) }