$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

That command will generate code in `./src/client` and will use `axios` (the frontend HTTP library) internally.

### Try Out the Client Code

```console
$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

此命令将在 `./src/client` 中生成代码，并将在其内部使用 `axios`（前端HTTP库）。

### 尝试客户端代码

现在您可以导入并使用客户端代码，它可能看起来像这样，请注意，您可以为这些方法使用自动补全：

<img src="/img/tutorial/generate-clients/image02.png">

您还将自动补全要发送的数据：

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

Dieser Befehl generiert Code in `./src/client` und verwendet intern `axios` (die Frontend-HTTP-Bibliothek).

### Den Client-Code ausprobieren

```console
$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

👈 📋 🔜 🏗 📟 `./src/client` &amp; 🔜 ⚙️ `axios` (🕸 🇺🇸🔍 🗃) 🔘.

### 🔄 👅 👩‍💻 📟

🔜 👆 💪 🗄 &amp; ⚙️ 👩‍💻 📟, ⚫️ 💪 👀 💖 👉, 👀 👈 👆 🤚 ✍ 👩‍🔬:

<img src="/img/tutorial/generate-clients/image02.png">

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

      } else {
        // Generalize differing axis lengths to "variable"/"unknown".
        for (int axis_index = 0; axis_index < combined_shape.dims();
             ++axis_index) {
          int64_t axis_length = combined_shape.dim_size(axis_index);
          if (axis_length != component_shape.dim_size(axis_index)) {
            axis_length = -1;
          }
          TF_RETURN_IF_ERROR(

TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "Min", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, input, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, axis, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

        adjust *= 31;
        adjust = ~~adjust;
        // in GWT, we have to deal with integer overflow carefully
      }
      int hash = 1;
      for (Set<E> axis : axes) {
        hash = 31 * hash + (size() / axis.size() * axis.hashCode());

        hash = ~~hash;
      }
      hash += adjust;
      return ~~hash;
    }
  }

  /**

`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:
`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`