code: def add_noise(signal, snr, S0, noise_type="rician", rng=None):
      r"""Add noise of specified distribution to the signal from a single voxel.
  
      Parameters
      ----------
      signal : 1-d ndarray
          The signal in the voxel.
      snr : float
          The desired signal-to-noise ratio. (See notes below.)
          If `snr` is None, return the signal as-is.
      S0 : float
          Reference signal for specifying `snr`.
      noise_type : string, optional
          The distribution of noise added. Can be either 'gaussian' for Gaussian
          distributed noise, 'rician' for Rice-distributed noise (default) or
          'rayleigh' for a Rayleigh distribution.
      rng : numpy.random.Generator class, optional
          Numpy's random generator for setting seed values when needed.
          Default is None.
  
      Returns
      -------
      signal : array, same shape as the input
          Signal with added noise.
  
      Notes
      -----
      SNR is defined here, following [1]_, as ``S0 / sigma``, where ``sigma`` is
      the standard deviation of the two Gaussian distributions forming the real
      and imaginary components of the Rician noise distribution (see [2]_).
  
      References
      ----------
      .. [1] Descoteaux, Angelino, Fitzgibbons and Deriche (2007) Regularized,
             fast and robust q-ball imaging. MRM, 58: 497-510
      .. [2] Gudbjartson and Patz (2008). The Rician distribution of noisy MRI
             data. MRM 34: 910-914.
  
      Examples
      --------
      >>> signal = np.arange(800).reshape(2, 2, 2, 100)
      >>> signal_w_noise = add_noise(signal, 10., 100., noise_type='rician')
  
      """
      if snr is None:
          return signal
  
      if rng is None:
          rng = np.random.default_rng()
  
      sigma = S0 / snr
  
      noise_adder = {
          "gaussian": _add_gaussian,
          "rician": _add_rician,
          "rayleigh": _add_rayleigh,
      }
  
      noise1 = rng.normal(0, sigma, size=signal.shape)
  
      if noise_type == "gaussian":
          noise2 = None
      else:
          noise2 = rng.normal(0, sigma, size=signal.shape)
  
      return noise_adder[noise_type](signal, noise1, noise2)
  
imports: import numpy
  from numpy import dot
  from dipy.core.geometry import sphere2cart
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.core.gradients import GradientTable
  from dipy.reconst.utils import dki_design_matrix
  from scipy.special import jn
label_desc: Use image node_id 5 for calling a global function with example usage: add_noise(signal, snr, S0, noise_type, rng) and returns: noise_adder, signal