"13.7.9 UNIMPLEMENTED"
"2.1 OK"
"2.10 OK"
"2.11 OK"
"2.2 OK"
"2.3 OK"
"2.4 OK"
"2.5 OK"
"2.6 OK"
"2.7 OK"
"2.8 OK"
"2.9 OK"
"3.1 OK"
"3.2 NON-STRICT"
"3.3 NON-STRICT"
"3.4 NON-STRICT"
"3.5 OK"
"3.6 OK"
"3.7 OK"
"4.1.1 OK"
"4.1.2 OK"
"4.1.3 NON-STRICT"
"4.1.4 NON-STRICT"
"4.1.5 OK"
"4.2.1 OK"
"4.2.2 OK"
"4.2.3 NON-STRICT"
"4.2.4 OK"
"4.2.5 OK"

    val data = Buffer()

    while (true) {
      when (source.select(options)) {
        in 0..2 -> {
          completeEvent(id, type, data)
          return true
        }

        in 3..4 -> {
          source.readData(data)
        }

        in 5..7 -> {
          data.writeByte('\n'.code) // 'data' on a line of its own.
        }

        in 8..9 -> {

        .containsExactly(list(1, 2), list(1, 3))
        .inOrder();
  }

  public void testCartesianProduct_binary2x2() {
    assertThat(cartesianProduct(list(1, 2), list(3, 4)))
        .containsExactly(list(1, 3), list(1, 4), list(2, 3), list(2, 4))
        .inOrder();
  }

  public void testCartesianProduct_2x2x2() {
    assertThat(cartesianProduct(list(0, 1), list(0, 1), list(0, 1)))

  }

  public void testImmutableCopy_empty() {
    assertThat(union(emptySet(), emptySet()).immutableCopy()).isEmpty();
    assertThat(intersection(newHashSet(1, 2), newHashSet(3, 4)).immutableCopy()).isEmpty();
    assertThat(difference(newHashSet(1, 2), newHashSet(1, 2)).immutableCopy()).isEmpty();
    assertThat(symmetricDifference(newHashSet(1, 2), newHashSet(1, 2)).immutableCopy()).isEmpty();
  }

比如，`item.dict(exclude_unset=True)`。

这段代码生成的 `dict` 只包含创建 `item` 模型时显式设置的数据，而不包括默认值。

然后再用它生成一个只含已设置（在请求中所发送）数据，且省略了默认值的 `dict`：

{* ../../docs_src/body_updates/tutorial002.py hl[34] *}

### 使用 Pydantic 的 `update` 参数

接下来，用 `.copy()` 为已有模型创建调用 `update` 参数的副本，该参数为包含更新数据的 `dict`。

例如，`stored_item_model.copy(update=update_data)`：

{* ../../docs_src/body_updates/tutorial002.py hl[35] *}

        "R0.00, R1.38, R1.13", // #3, after that the rate changes
        "R0.88", // #4, this is what the throttling would be with the old rate
        "R0.34, R0.28, R0.25, R0.25", // #5
        "U4.25", // #6
        "R0.00, R0.72, R0.66, R0.59, R0.53, R0.47, R0.41", // #7
        "R0.34, R0.28, R0.25, R0.25"); // #7 (cont.), note, this matches #5
  }

  public void testWarmUpAndUpdateWithColdFactor() {

        "R0.00, R1.38, R1.13", // #3, after that the rate changes
        "R0.88", // #4, this is what the throttling would be with the old rate
        "R0.34, R0.28, R0.25, R0.25", // #5
        "U4.25", // #6
        "R0.00, R0.72, R0.66, R0.59, R0.53, R0.47, R0.41", // #7
        "R0.34, R0.28, R0.25, R0.25"); // #7 (cont.), note, this matches #5
  }

  public void testWarmUpAndUpdateWithColdFactor() {

<img src="/img/tutorial/security/image03.png">

Мы пока не проверяем валидность токена, но для начала это уже неплохо.

## Резюме { #recap }

		wantpos int64
		readerr error
		seekerr string
	}{
		{seek: io.SeekStart, off: 0, n: 20, want: "0123456789"},
		{seek: io.SeekStart, off: 1, n: 1, want: "1"},
		{seek: io.SeekCurrent, off: 1, wantpos: 3, n: 2, want: "34"},
		{seek: io.SeekStart, off: -1, seekerr: "bytes.Reader.Seek: negative position"},
		{seek: io.SeekStart, off: 1 << 33, wantpos: 1 << 33, readerr: io.EOF},
		{seek: io.SeekCurrent, off: 1, wantpos: 1<<33 + 1, readerr: io.EOF},

  CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), make_shape({2, 4})},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, 4}));

  // Infer shape when second operand has unknown shape.
  CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), unknown_shape()},
                    /*input_tensors*/ {},