def METHOD_NAME(time_series_sensor_data: np.ndarray,
                                                      detection_geometry: DetectionGeometryBase,
                                                      speed_of_sound_in_m_per_s: int = 1540,
                                                      time_spacing_in_s: float = 2.5e-8,
                                                      sensor_spacing_in_mm: float = 0.1,
                                                      recon_mode: str = Tags.RECONSTRUCTION_MODE_PRESSURE,
                                                      apodization: str = Tags.RECONSTRUCTION_APODIZATION_BOX
                                                      ) -> np.ndarray:
    """
    Convenience function for reconstructing time series data using Delay and Sum algorithm implemented in PyTorch
    :param time_series_sensor_data: (2D numpy array) sensor data of shape (sensor elements, time steps)
    :param speed_of_sound_in_m_per_s: (int) speed of sound in medium in meters per second (default: 1540 m/s)
    :param time_spacing_in_s: (float) time between sampling points in seconds (default: 2.5e-8 s which is equal to 40 MHz)
    :param sensor_spacing_in_mm: (float) space between sensor elements in millimeters (default: 0.1 mm)
    :param recon_mode: SIMPA Tag defining the reconstruction mode - pressure default OR differential
    :param apodization: SIMPA Tag defining the apodization function (default box)
    :return: (2D numpy array) reconstructed image as 2D numpy array
    """
    # create settings
    settings = create_reconstruction_settings(speed_of_sound_in_m_per_s, time_spacing_in_s, sensor_spacing_in_mm,
                                              recon_mode, apodization)
    adapter = SignedDelayMultiplyAndSumAdapter(settings)
    return adapter.reconstruction_algorithm(time_series_sensor_data, detection_geometry)