code: def _multi_mass_absolute(
      Q, T, m, Q_subseq_isfinite, T_subseq_isfinite, p=2.0
  ):
      """
      A multi-dimensional wrapper around "Mueen's Algorithm for Similarity Search"
      (MASS) absolute to compute multi-dimensional non-normalized (i.e., without
      z-normalization distance profile.
  
      Parameters
      ----------
      Q : numpy.ndarray
          Query array or subsequence
  
      T : numpy.ndarray
          Time series array or sequence
  
      m : int
          Window size
  
      Q_subseq_isfinite : numpy.ndarray
          A boolean array that indicates whether the subsequence in `Q` contains a
          `np.nan`/`np.inf` value (False)
  
      T_subseq_isfinite : numpy.ndarray
          A boolean array that indicates whether a subsequence in `T` contains a
          `np.nan`/`np.inf` value (False)
  
      p : float, default 2.0
          The p-norm to apply for computing the Minkowski distance. Minkowski distance is
          typically used with `p` being 1 or 2, which correspond to the Manhattan distance
          and the Euclidean distance, respectively.
  
      Returns
      -------
      D : numpy.ndarray
          Multi-dimensional non-normalized (i.e., without z-normalization) distance
          profile
      """
      d, n = T.shape
      l = n - m + 1
  
      D = np.empty((d, l), dtype=np.float64)
  
      for i in range(d):
          if np.any(~Q_subseq_isfinite[i]):
              D[i, :] = np.inf
          else:
              D[i, :] = core.mass_absolute(Q[i], T[i], p=p)
  
          D[i][~(T_subseq_isfinite[i])] = np.inf
  
      return D
  
file_path: stumpy/stumpy/maamp.py
imports: from functools import partial
  import numpy
  from numba import njit, prange
  from .None import config, core
label_desc: Use image node_id 1 for calling a global function with example usage: _multi_mass_absolute(Q, T, m, Q_subseq_isfinite, T_subseq_isfinite, p) and returns: D