headers: []*Header{{
			Name:       "test2/",
			Mode:       16877,
			Uid:        1000,
			Gid:        1000,
			Size:       14,
			ModTime:    time.Unix(1441973427, 0),
			Typeflag:   'D',
			Uname:      "rawr",
			Gname:      "dsnet",
			AccessTime: time.Unix(1441974501, 0),
			ChangeTime: time.Unix(1441973436, 0),
			Format:     FormatGNU,
		}, {
			Name:       "test2/foo",
			Mode:       33188,

		tests: []testFnc{
			testHeader{Header{
				Typeflag: TypeReg,
				Name:     "hi\x80\x81\x82\x83bye",
				Mode:     0644,
				Uid:      1000,
				Gid:      1000,
				Uname:    "rawr",
				Gname:    "dsnet",
				ModTime:  time.Unix(0, 0),
				Format:   FormatGNU,
			}, nil},
			testClose{nil},
		},
		// TODO(dsnet): Re-enable this test when adding sparse support.

heige <******@****.***> 1638149600 +0800

Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.
Typically, enabling compression on spinning disk systems
will increase speed when the content can be compressed.

  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>(

    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

  {
    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);