code: def _get_multi_p_norm(start, T, m, p=2.0):
      """
      Multi-dimensional wrapper to compute the p-norm between the
      query, `T[:, start:start+m])` and the time series, `T`. Additionally, compute
      p-norm for the first window.
  
      Parameters
      ----------
      start : int
          The window index for T_B from which to calculate the QT dot product
  
      T : numpy.ndarray
          The time series or sequence for which to compute the dot product
  
      m : int
          Window size
  
      p : float
          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
      -------
      p_norm : numpy.ndarray
          Given `start`, return the corresponding multi-dimensional p-norm
  
      p_norm_first : numpy.ndarray
          Multi-dimensional p-norm for the first window
      """
      d = T.shape[0]
      l = T.shape[1] - m + 1
  
      p_norm = np.empty((d, l), dtype=np.float64)
      p_norm_first = np.empty((d, l), dtype=np.float64)
      for i in range(d):
          p_norm[i] = np.power(
              core.mass_absolute(T[i, start : start + m], T[i], p=p), p
          )
          p_norm_first[i] = np.power(
              core.mass_absolute(T[i, :m], T[i], p=p), p
          )
  
      return p_norm, p_norm_first
  
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 8 for calling a global function with example usage: _get_multi_p_norm(start, T, m, p) and returns: p_norm,  p_norm_first