You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

                dstIndex += 4;
                SMBUtil.writeInt4(0, dst, dstIndex);
                dstIndex += 4; // Reserved
                final int dataLen = size8(nc.encode(dst, dstIndex));
                SMBUtil.writeInt2(dataLen, dst, lenOffset);
                dstIndex += dataLen;
            }
        }
        return dstIndex - start;
    }

    /**
     * {@inheritDoc}
     *

                final int dataLen = SMBUtil.readInt2(buffer, ncpos + 2);

                // Validate context data length
                if (dataLen < 0 || dataLen > 1024) { // 1KB max per context
                    throw new SMBProtocolDecodingException(
                            "Invalid negotiate context data length: " + dataLen + " at position " + i + " (must be 0-1024)");
                }

				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),
				testCase.accessKey, testCase.secretKey)
		case testCase.contentEncoding == "":
			req, err = newTestStreamingSignedRequest(http.MethodPut,
				getPutObjectURL("", testCase.bucketName, testCase.objectName),
				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),

}

// calculateSignedChunkLength - calculates the length of the overall stream (data + metadata)
func calculateStreamContentLength(dataLen, chunkSize int64) int64 {
	if dataLen <= 0 {
		return 0
	}
	chunksCount := dataLen / chunkSize
	remainingBytes := dataLen % chunkSize
	var streamLen int64
	streamLen += chunksCount * calculateSignedChunkLength(chunkSize)
	if remainingBytes > 0 {

        components.
    *   Improve the performance of datasets using `from_tensors()`.
    *   Promoting `unbatch` from experimental to core API.
    *   Adding support for datasets as inputs to `from_tensors` and
        `from_tensor_slices` and batching and unbatching of nested datasets.

*   `tf.distribute`: