.`when`(
          request().withPath("/person")
            .withQueryStringParameter("name", "peter"),
        )
        .respond(response().withBody("Peter the person!"))

      val results =
        (1..20).map {
          executor.submit {
            val response =
              client.newCall(Request((mockServer.secureEndpoint + "/person?name=peter").toHttpUrl())).execute()

### Classes como tipos

Você também pode declarar uma classe como o tipo de uma variável.

Digamos que você tenha uma classe `Person`, com um nome:

```Python hl_lines="1-3"
{!../../docs_src/python_types/tutorial010.py!}
```

Então você pode declarar que uma variável é do tipo `Person`:

```Python hl_lines="6"
{!../../docs_src/python_types/tutorial010.py!}
```

                    var reason = prompt("Enter a reason for accepting these changes:");
                    if (!reason) {
                        alert("You must enter a reason to accept all changes.");
                        return;
                    }

                    var textToWrite = JSON.stringify(appendErrorCorrections(reason), null, 4) + "\\n";

  int version = TFE_CUSTOM_DEVICE_VERSION;
  // Method to copy a tensor to the custom device.
  TFE_TensorHandle* (*copy_tensor_to_device)(TFE_Context* context,
                                             TFE_TensorHandle* tensor,
                                             TF_Status* status,
                                             void* device_info);

  // Method to copy a tensor from the custom device to a target device.

                                                   size_t* len);

// On success, dequeues a tensor from a TF-managed FifoQueue given by
// `tensor_id`, associated with `session`. There must be a graph node named
// "fifo_queue_dequeue_<tensor_id>", to be executed by this API call.

// Caller must call TF_DeleteTensor() over the returned tensor. If the queue is
// empty, this call is blocked.
//

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

                          const char* name) {
  unique_tensor_ptr tensor(BoolTensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
                          const char* name) {
  unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

    System.out.println("MESSAGE: " + bytes.hex());
  }

  @Override public void onClosing(WebSocket webSocket, int code, String reason) {
    webSocket.close(1000, null);
    System.out.println("CLOSE: " + code + " " + reason);
  }

  @Override public void onFailure(WebSocket webSocket, Throwable t, Response response) {
    t.printStackTrace();
  }

class Person:
    def __init__(self, name: str):
        self.name = name


def get_person_name(one_person: Person):