// TF_TensorToTensorV1 decodes a string serialization to DT_RESOURCE.
// In the TFv1 convention, TF_Tensor can hold a string serialization of
// DT_RESOURCE. The string serialization is converted back to a
// ResourceHandle during Session run where the TF_Tensor is converted to a
// Tensor.
// TFv2 does not depend on this conversion. There is no matching
// TF_TensorFromTensorV1 because the conversion to string is performed by the

     * @return the value as an object
     */
    Object getAsObject(String value);

    /**
     * Returns {@literal true} if this converter can handle the specified type.
     *
     * @param clazz
     *            the type. Must not be {@literal null}
     * @return {@literal true} if this converter can handle the specified type
     */
    boolean isTarget(Class<?> clazz);

	var xl xlMetaV2
	err := xl.Load(data)
	if err != nil {
		t.Fatal(err)
	}
	v0 := xl.versions[0]

	// Saved with signature 0xfe, 0x54, 0xbc, 0x2f
	// Signature must be converted after load.
	wantSig := [4]byte{0x1e, 0x5f, 0xba, 0x4a}
	if v0.header.Signature != wantSig {
		t.Errorf("Wrong signature, want %#v, got %#v", wantSig, v0.header.Signature)
	}
	v, err := xl.getIdx(0)

        ReadingConverter converter2 = new TestReadingConverter();

        converter1.init();
        converter2.init();

        String text = "test";
        String field = "content";

        List<String> readings1 = converter1.convert(text, field, "en");
        List<String> readings2 = converter2.convert(text, field, "ja");

        assertNotNull(readings1);
        assertNotNull(readings2);

// Create a TF_Function from a TF_Graph
//
// Params:
//  fn_body - the graph whose operations (or subset of whose operations) will be
//            converted to TF_Function.
//  fn_name - the name of the new TF_Function. Should match the operation
//            name (OpDef.name) regexp [A-Z][A-Za-z0-9_.\\-/]*.

        List<String> results = converter.convert(text, field, "en");

        assertNotNull(results);
    }

    @Test
    public void testMultipleConverterInstances() throws IOException {
        // Test that multiple converter instances work independently
        AnalyzerConverter converter1 = new AnalyzerConverter(client, settings);
        AnalyzerConverter converter2 = new AnalyzerConverter(client, settings);

    /** The key of the message: {item} must be a number. */
    public static final String CONSTRAINTS_TypeDouble_MESSAGE = "{constraints.TypeDouble.message}";

    /** The key of the message: {item} cannot be converted to {propertyType}. */
    public static final String CONSTRAINTS_TypeAny_MESSAGE = "{constraints.TypeAny.message}";

    /** The key of the message: {item} has an unrecognized URI. */

Por exemplo, você não pode colocar um modelo do Pydantic em uma `JSONResponse` sem antes convertê-lo em um `dict` com todos os tipos de dados (como `datetime`, `UUID`, etc) convertidos para tipos compatíveis com JSON.

Para esses casos, você pode usar o `jsonable_encoder` para converter seus dados antes de repassá-los para a resposta:

{* ../../docs_src/response_directly/tutorial001.py hl[6:7,21:22] *}

	data xlMetaInlineData

	// metadata version.
	metaV uint8
}

// LoadOrConvert will load the metadata in the buffer.
// If this is a legacy format, it will automatically be converted to XLV2.
func (x *xlMetaV2) LoadOrConvert(buf []byte) error {
	if isXL2V1Format(buf) {
		return x.Load(buf)
	}

	xlMeta := &xlMetaV1Object{}
	json := jsoniter.ConfigCompatibleWithStandardLibrary

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code