code: def __init__(
      self,
      data,
      mask=None,
      model="3k",
      niters=25,
      ngb_size=6,
      beta=0.5,
      ref_params=None,
      init_params=None,
      convert=True,
  ):
      self.labels = ("CSF", "GM", "WM")
      self.data = data
      self.mask = mask
  
      mixmat = np.asarray(model)
      if mixmat.ndim == 2:
          nclasses = mixmat.shape[0]
          if nclasses < 3:
              raise ValueError("at least 3 classes required")
          if not mixmat.shape[1] == 3:
              raise ValueError("mixing matrix should have 3 rows")
          self.mixmat = mixmat
      elif model == "3k":
          self.mixmat = np.eye(3)
      elif model == "4k":
          self.mixmat = np.array(
              [
                  [1.0, 0.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 0.0, 1.0],
              ]
          )
      elif model == "5k":
          self.mixmat = np.array(
              [
                  [1.0, 0.0, 0.0],
                  [1.0, 0.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 0.0, 1.0],
              ]
          )
      else:
          raise ValueError("unknown brain segmentation model")
  
      self.niters = int(niters)
      self.beta = float(beta)
      self.ngb_size = int(ngb_size)
  
      # Class parameter initialization
      if init_params is None:
          if ref_params is None:
              ref_params = T1_ref_params
          self.init_mu, self.init_sigma = self._init_parameters(
              ref_params
          )
      else:
          self.init_mu = np.array(init_params[0], dtype="double")
          self.init_sigma = np.array(init_params[1], dtype="double")
          if (
              not len(self.init_mu) == self.mixmat.shape[0]
              or not len(self.init_sigma) == self.mixmat.shape[0]
          ):
              raise ValueError(
                  "Inconsistent initial parameter estimates"
              )
  
      self._run()
      if convert:
          self.convert()
      else:
          self.label = map_from_ppm(self.ppm, self.mask)
  
imports: import numpy
  from .segmentation import Segmentation, map_from_ppm, moment_matching
label_desc: Use image node_id 1 to create a new BrainT1Segmentation object  with example: obj = BrainT1Segmentation(data, mask, model, niters, ngb_size, beta, ref_params, init_params, convert)