```JSON
{
  "detail": [
    {
      "type": "int_parsing",
      "loc": [
        "path",
        "item_id"
      ],
      "msg": "Input should be a valid integer, unable to parse string as an integer",
      "input": "foo"
    }
  ]
}
```

porque o parâmetro de path `item_id` tinha o valor `"foo"`, que não é um `int`.

```JSON
{
  "detail": [
    {
      "type": "int_parsing",
      "loc": [
        "path",
        "item_id"
      ],
      "msg": "Input should be a valid integer, unable to parse string as an integer",
      "input": "foo"
    }
  ]
}
```

这是因为路径参数 `item_id` 的值（`"foo"`）的类型不是 `int`。

        // Default constructor
    }

    static int id = 0;
    static LogStream log = LogStream.getInstance();

    /**
     * Reads exactly len bytes from the input stream into the buffer.
     *
     * @param in the input stream to read from
     * @param b the buffer to read into
     * @param off the offset in the buffer to start writing
     * @param len the number of bytes to read

                return null;
            }
        }
        assertThrows(ClassCastException.class, () -> {
            OtherPipeHandle result = target.unwrap(OtherPipeHandle.class);
        });

        // Null input: document current behavior (NPE from isAssignableFrom)
        assertThrows(NullPointerException.class, () -> target.unwrap(null));
    }

    @Test

		object_context = event["getObjectContext"]

		# Get the presigned URL to fetch the requested
		# original object from MinIO
		s3_url = object_context["inputS3Url"]

		# Extract the route and request token from the input context
		request_route = object_context["outputRoute"]
		request_token = object_context["outputToken"]

		# Get the original S3 object using the presigned URL
		r = requests.get(s3_url)

                result = result.replace(ESCAPED_QUOTE, "\"");
            }
        }

        return result;

    }

    /**
     * Quote and escape input value for CSV
     *
     * @param original Original text.
     * @return Escaped text.
     */
    public static String quoteEscape(final String original) {
        String result = original;

                                            key="labels.dict_synonym_file"/></label>
                                    <div class="col-sm-9">
                                        <input type="file" id="synonymFile" name="synonymFile" class="form-control-file"/>
                                    </div>
                                </div>
                            </div>

                                            key="labels.dict_protwords_file"/></label>
                                    <div class="col-sm-9">
                                        <input type="file" id="protwordsFile" name="protwordsFile" class="form-control-file"/>
                                    </div>
                                </div>
                            </div>

    @Nullable A[] result =
        FluentIterable.from(annotations).filter(annotationType).toArray(annotationType);
    @SuppressWarnings("nullness") // safe because the input list contains no nulls
    A[] cast = (A[]) result;
    return cast;
  }

  @Override
  public boolean equals(@Nullable Object obj) {
    if (obj instanceof Parameter) {

你也可以在模型設定中搭配 `ser_json_bytes` 使用 `bytes` 欄位來處理輸出資料；當產生 JSON 回應時，Pydantic 會將位元組以 base64 進行序列化。

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

## Pydantic `bytes` 用於輸入與輸出資料 { #pydantic-bytes-for-input-and-output-data }

當然，你也可以使用同一個以 base64 設定的模型，同時處理輸入（以 `val_json_bytes` 驗證）與輸出（以 `ser_json_bytes` 序列化）的 JSON 資料。