if !sufficientCapacity {
		writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, AdminError{
			Code:       "XMinioSpeedtestInsufficientCapacity",
			Message:    capacityErrMsg,
			StatusCode: http.StatusInsufficientStorage,
		}), r.URL)
		return
	}

	if autotune && !canAutotune {
		autotune = false
	}

	if customBucket == "" {

)

const speedTest = "speedtest"

type speedTestOpts struct {
	objectSize       int
	concurrencyStart int
	concurrency      int
	duration         time.Duration
	autotune         bool
	storageClass     string
	bucketName       string
	enableSha256     bool
	enableMultipart  bool
	creds            auth.Credentials
}

// Get the max throughput and iops numbers.

        behavior, set `inject_prefetch=True` in
        `tf.data.experimental.OptimizationOptions`.
    *   Added a new value to `tf.data.Options.autotune.autotune_algorithm`:
        `STAGE_BASED`. If the autotune algorithm is set to `STAGE_BASED`, then
        it runs a new algorithm that can get the same performance with lower
        CPU/memory usage.
    *   Added

@app.put("/user/{user_id}")
def put_user(user_id: str, name: str = Body(), db: dict = Depends(get_database)):
    db[user_id] = name
    return {"message": "OK"}


@pytest.fixture(autouse=True)
def reset_state_and_db():
    global fake_database
    global state
    fake_database = initial_fake_database.copy()
    state = initial_state.copy()


client = TestClient(app)