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"`

		t.Errorf("got %s; want %s", toJSONString(got), toJSONString(want))
	}
}

func TestManyToManyPreloadWithMultiPrimaryKeys(t *testing.T) {
	if name := DB.Dialector.Name(); name == "sqlite" || name == "sqlserver" {
		t.Skip("skip sqlite, sqlserver due to it doesn't support multiple primary keys with auto increment")
	}

	type (
		Level1 struct {
			ID           uint   `gorm:"primary_key;"`

	sql = strings.ReplaceAll(sql, `'`, `"`)

	// convert dialect special quote into double quote
	switch DB.Dialector.Name() {
	case "postgres":
		sql = strings.ReplaceAll(sql, `"`, `"`)
	case "mysql", "sqlite":
		sql = strings.ReplaceAll(sql, "`", `"`)
	case "sqlserver":
		sql = strings.ReplaceAll(sql, `'`, `"`)
	}

	return sql

							t.Errorf("invalid data type for %v, got %v %#v", dbName, resultType, first[dbName])
						}
					case "Birthday":
						if !strings.Contains(resultType, "Time") && !(DB.Dialector.Name() == "sqlite" && strings.Contains(resultType, "string")) {
							t.Errorf("invalid data type for %v, got %v %#v", dbName, resultType, first[dbName])
						}
					}

					reflectValue := reflect.Indirect(reflect.ValueOf(users[0]))

      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,

       *   `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`

	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) }

In diesem Fall möchten Sie möglicherweise dokumentieren, wie diese externe API aussehen *sollte*. Welche *Pfadoperation* sie haben sollte, welchen Body sie erwarten sollte, welche Response sie zurückgeben sollte, usw.

## Eine Anwendung mit Callbacks

Sehen wir uns das alles anhand eines Beispiels an.