code: def _mm_assignment(X, barycenter, weights, metric_params=None):
      """Computes item assignement based on DTW alignments and return cost as a
      bonus.
  
      Parameters
      ----------
      X : numpy.array of shape (n, sz, d)
          Time-series to be averaged
  
      barycenter : numpy.array of shape (barycenter_size, d)
          Barycenter as computed at the current step of the algorithm.
  
      weights: array
          Weights of each X[i]. Must be the same size as len(X).
  
      metric_params: dict or None (default: None)
          DTW constraint parameters to be used.
          See :ref:`tslearn.metrics.dtw_path <fun-tslearn.metrics.dtw_path>` for
          a list of accepted parameters
          If None, no constraint is used for DTW computations.
  
      Returns
      -------
      list of index pairs
          Warping paths
  
      float
          Current alignment cost
      """
      if metric_params is None:
          metric_params = {}
      n = X.shape[0]
      cost = 0.0
      list_p_k = []
      for i in range(n):
          path, dist_i = dtw_path(barycenter, X[i], **metric_params)
          cost += dist_i**2 * weights[i]
          list_p_k.append(path)
      cost /= weights.sum()
      return list_p_k, cost
  
imports: import numpy
  import warnings
  from scipy.interpolate import interp1d
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils import check_random_state
  from ..metrics import dtw_path
  from ..utils import to_time_series_dataset, ts_size
  from .utils import _set_weights
label_desc: Use image node_id 6 for calling a global function with example usage: _mm_assignment(X, barycenter, weights, metric_params) and returns: list_p_k,  cost