{
    AbstractContext* ctx_raw = nullptr;
    Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 2.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s =
        TestScalarTensorHandle<float, TF_FLOAT>(ctx_.get(), 2.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  AbstractTensorHandlePtr y;
  {
    AbstractTensorHandle* y_raw = nullptr;
    Status s =
        TestScalarTensorHandle<float, TF_FLOAT>(ctx_.get(), 7.0f, &y_raw);

    ASSERT_EQ(errors::OK, status_.code()) << status_.message();

    {
      AbstractContext* ctx_raw = nullptr;
      status_ =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically

Refer to the dashboard [json file here](https://raw.githubusercontent.com/minio/minio/master/docs/metrics/prometheus/grafana/minio-dashboard.json).

![Grafana](https://raw.githubusercontent.com/minio/minio/master/docs/metrics/prometheus/grafana/grafana-minio.png)

            - background
            - raw_headers
            - render
            - init_headers
            - headers
            - set_cookie
            - delete_cookie

::: fastapi.responses.ORJSONResponse
    options:
        members:
            - charset
            - status_code
            - media_type
            - body
            - background
            - raw_headers
            - render

  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 2.0f, &x_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    x.reset(x_raw);
  }

  AbstractTensorHandlePtr y;
  {
    AbstractTensorHandle* y_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(

            - background
            - raw_headers
            - render
            - init_headers
            - headers
            - set_cookie
            - delete_cookie

::: fastapi.responses.ORJSONResponse
    options:
        members:
            - charset
            - status_code
            - media_type
            - body
            - background
            - raw_headers
            - render

  AbstractTensorHandlePtr x1;
  {
    AbstractTensorHandle* x1_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 1.0f, &x1_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    x1.reset(x1_raw);
  }

  AbstractTensorHandlePtr x2;
  {
    AbstractTensorHandle* x2_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(

//
// +k8s:deepcopy-gen=true
// +protobuf=true
// +k8s:openapi-gen=true
message RawExtension {
  // Raw is the underlying serialization of this object.
  //
  // TODO: Determine how to detect ContentType and ContentEncoding of 'Raw' data.
  optional bytes raw = 1;
}

// TypeMeta is shared by all top level objects. The proper way to use it is to inline it in your type,
// like this:
//

  }

  private fun serveConnection(raw: Socket) {
    taskRunner.newQueue().execute("MockWebServer ${raw.remoteSocketAddress}", cancelable = false) {
      try {
        SocketHandler(raw).handle()
      } catch (e: IOException) {
        logger.fine("$this connection from ${raw.inetAddress} failed: $e")
      } catch (e: Exception) {
        logger.log(Level.SEVERE, "$this connection from ${raw.inetAddress} crashed", e)
      }
    }