code: def mean_signal_bvalue(data, gtab, bmag=None):
      """
      Computes the average signal across different diffusion directions
      for each unique b-value
  
      Parameters
      ----------
      data : ndarray ([X, Y, Z, ...], g)
          ndarray containing the data signals in its last dimension.
      gtab : a GradientTable class instance
          The gradient table containing diffusion acquisition parameters.
      bmag : The order of magnitude that the bvalues have to differ to be
          considered an unique b-value. Default: derive this value from the
          maximal b-value provided: $bmag=log_{10}(max(bvals)) - 1$.
  
      Returns
      -------
      msignal : ndarray ([X, Y, Z, ..., nub])
          Mean signal along all gradient directions for each unique b-value
          Note that the last dimension contains the signal means and nub is the
          number of unique b-values.
      ng : ndarray(nub)
          Number of gradient directions used to compute the mean signal for
          all unique b-values
  
      Notes
      -----
      This function assumes that directions are evenly sampled on the sphere or
      on the hemisphere
      """
      bvals = gtab.bvals.copy()
  
      # Compute unique and rounded bvals
      ub, rb = unique_bvals_magnitude(bvals, bmag=bmag, rbvals=True)
  
      # Initialize msignal and ng
      nub = ub.size
      ng = np.zeros(nub)
      msignal = np.zeros(data.shape[:-1] + (nub,))
      for bi in range(ub.size):
          msignal[..., bi] = np.mean(data[..., rb == ub[bi]], axis=-1)
          ng[bi] = np.sum(rb == ub[bi])
      return msignal, ng
  
imports: import numpy
  import scipy.optimize
  from dipy.core.gradients import check_multi_b, unique_bvals_magnitude, round_bvals
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import MIN_POSITIVE_SIGNAL
  from dipy.core.ndindex import ndindex
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 1 for calling a global function with example usage: mean_signal_bvalue(data, gtab, bmag) and returns: msignal,  ng