public List<String> getResult() {
            return result;
          }
        });
  }

  /**
   * Copies all bytes from a URL to an output stream.
   *
   * @param from the URL to read from
   * @param to the output stream
   * @throws IOException if an I/O error occurs
   */
  public static void copy(URL from, OutputStream to) throws IOException {
    asByteSource(from).copyTo(to);
  }

package org.codelibs.curl.io;

import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.util.logging.Logger;

import org.apache.commons.io.output.DeferredFileOutputStream;

/**
 * ContentOutputStream is a custom output stream that extends DeferredFileOutputStream.
 * It writes data to a temporary file once the data size exceeds a specified threshold.
 *

  "content": "hello"
}
```

## 用于输出数据的 Pydantic `bytes` { #pydantic-bytes-for-output-data }

对于输出数据，你也可以在模型配置中为 `bytes` 字段使用 `ser_json_bytes`，Pydantic 会在生成 JSON 响应时将字节以 base64 进行序列化。

{* ../../docs_src/json_base64_bytes/tutorial001_py310.py ln[1:2,12:16,29,38:41] hl[16] *}

## 用于输入和输出数据的 Pydantic `bytes` { #pydantic-bytes-for-input-and-output-data }

	input       chan *queueItem           // input for workers
	queue       chan *queueItem           // output from workers in order
	err         error                     // global error state, only touched by Reader.Read
	bufferPool  bpool.Pool[[]byte]        // pool of []byte objects for input
	kvDstPool   bpool.Pool[[]jstream.KVS] // pool of []jstream.KVS used for output
	close       chan struct{}             // used for shutting down the splitter before end of stream

CHANGELOG.asciidoc  merge=union

# These files contain expected text output, and should not be changed on
# Windows

    add_output2 = TF_OutputListGet(add_outputs, 0);
    TF_DeleteOutputList(add_outputs);
  }

  // 3rd Output will be Matrix Multiplication of add_output1 and add_output2
  TF_AbstractTensor* mm_output;
  {
    // Build an abstract operation, inputs and output.
    auto* mm_op = TF_NewAbstractOp(graph_ctx);
    TF_AbstractOpSetOpType(mm_op, "MatMul", s);

   * char} are hashed directly (in that order). The input must not be updated while this method is
   * in progress.
   *
   * <p><b>Warning:</b> This method will produce different output than most other languages do when
   * running the same hash function on the equivalent input. For cross-language compatibility, use
   * {@link #putString}, usually with a charset of UTF-8. For other use cases, use {@code

    }

    /**
     * Tests that the getter for the output stream returns the correct stream.
     * @throws CIFSException if an error occurs while getting the stream.
     */
    @Test
    public void testGetOutputStream() throws CIFSException {
        assertEquals(mockOutputStream, smbPipeHandle.getOutput(), "getOutput() should return the correct output stream.");
    }

    /**

}
```

## 出力データ向けの Pydantic `bytes` { #pydantic-bytes-for-output-data }

出力データ用にモデル設定で `ser_json_bytes` とともに `bytes` フィールドを使用することもでき、Pydantic は JSON レスポンスを生成するときにバイト列を base64 でシリアライズします。

{* ../../docs_src/json_base64_bytes/tutorial001_py310.py ln[1:2,12:16,29,38:41] hl[16] *}

## 入力・出力データ向けの Pydantic `bytes` { #pydantic-bytes-for-input-and-output-data }

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.