code: def gpu_aampdist(
      T_A, T_B, m, percentage=0.05, k=None, device_id=0, p=2.0
  ):
      """
      Compute the non-normalized (i.e., without z-normalization) matrix profile distance
      (MPdist) measure between any two time series with one or more GPU devices and
      `stumpy.gpu_aamp`.
  
      The MPdist distance measure considers two time series to be similar if they share
      many subsequences, regardless of the order of matching subsequences. MPdist
      concatenates and sorts the output of an AB-join and a BA-join and returns the value
      of the `k`th smallest number as the reported distance. Note that MPdist is a
      measure and not a metric. Therefore, it does not obey the triangular inequality but
      the method is highly scalable.
  
      Parameters
      ----------
      T_A : numpy.ndarray
          The first time series or sequence for which to compute the matrix profile
  
      T_B : numpy.ndarray
          The second time series or sequence for which to compute the matrix profile
  
      m : int
          Window size
  
      percentage : float, default 0.05
          The percentage of distances that will be used to report `mpdist`. The value
          is between 0.0 and 1.0. This parameter is ignored when `k` is not `None`.
  
      k : int, default None
          Specify the `k`th value in the concatenated matrix profiles to return. When `k`
          is not `None`, then the `percentage` parameter is ignored.
  
      device_id : int or list, default 0
          The (GPU) device number to use. The default value is `0`. A list of
          valid device ids (int) may also be provided for parallel GPU-STUMP
          computation. A list of all valid device ids can be obtained by
          executing `[device.id for device in numba.cuda.list_devices()]`.
  
      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
      -------
      MPdist : float
          The matrix profile distance
  
      Notes
      -----
      `DOI: 10.1109/ICDM.2018.00119 \
      <https://www.cs.ucr.edu/~eamonn/MPdist_Expanded.pdf>`__
  
      See Section III
      """
      partial_mp_func = functools.partial(gpu_aamp, p=p)
      return _mpdist(
          T_A,
          T_B,
          m,
          partial_mp_func,
          percentage,
          k,
          device_id=device_id,
      )
  
imports: import functools
  from .None import gpu_aamp
  from .core import _mpdist
label_desc: Use image node_id 1 for calling a global function with example usage: gpu_aampdist(T_A, T_B, m, percentage, k, device_id, p) and returns: _mpdist