// categories is a list of the grouped resources this resource belongs to (e.g. 'all')
  repeated string categories = 7;

  // The hash value of the storage version, the version this resource is
  // converted to when written to the data store. Value must be treated
  // as opaque by clients. Only equality comparison on the value is valid.
  // This is an alpha feature and may change or be removed in the future.

	return err
}

// readGNUSparsePAXHeaders checks the PAX headers for GNU sparse headers.
// If they are found, then this function reads the sparse map and returns it.
// This assumes that 0.0 headers have already been converted to 0.1 headers
// by the PAX header parsing logic.
func (tr *Reader) readGNUSparsePAXHeaders(hdr *Header) (sparseDatas, error) {
	// Identify the version of GNU headers.
	var is1x0 bool

  // and "rules" only included `apiGroups:["apps"], apiVersions:["v1"], resources: ["deployments"]`,
  // a request to apps/v1beta1 or extensions/v1beta1 would be converted to apps/v1 and sent to the ValidatingAdmissionPolicy.
  //
  // Defaults to "Equivalent"
  // +optional
  optional string matchPolicy = 7;
}

// 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

	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)

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:

```Python hl_lines="6-7  21-22"
{!../../docs_src/response_directly/tutorial001.py!}

// 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_.\\-/]*.

---
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

            This must not be set in your local project, as it is updated by
            tools placing it in the repository. Valid values are: {@code none} (default),
            {@code converted} (repository manager converted this from an Maven 1 POM),
            {@code partner}
            (directly synced from a partner Maven 2 repository), {@code deployed} (was deployed from a Maven 2

            String s;
            while ((s = queue.poll()) != null && convertedTexts.size() <= getMaxReadingNum()) {
                final List<String> results = converter.convert(s, field, lang);
                convertedTexts.addAll(results);
            }
            queue.addAll(convertedTexts);
        }

        return convertedTexts;
    }

    public void addConverter(final ReadingConverter converter) {