class_docstr: Compute the Pan Matrix Profile with with one or more GPU devices
  
  This is based on the SKIMP algorithm.
  
  Parameters
  ----------
  T : numpy.ndarray
      The time series or sequence for which to compute the pan matrix profile
  
  m_start : int, default 3
      The starting (or minimum) subsequence window size for which a matrix profile
      may be computed
  
  m_stop : int, default None
      The stopping (or maximum) subsequence window size for which a matrix profile
      may be computed. When `m_stop = Non`, this is set to the maximum allowable
      subsequence window size
  
  m_step : int, default 1
      The step between subsequence window sizes
  
  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.
  
  Attributes
  ----------
  PAN_ : numpy.ndarray
      The transformed (i.e., normalized, contrasted, binarized, and repeated)
      pan matrix profile
  
  M_ : numpy.ndarray
      The full list of (breadth first search (level) ordered) subsequence window
      sizes
  
  Methods
  -------
  update():
      Compute the next matrix profile using the next available (breadth-first-search
      (level) ordered) subsequence window size and update the pan matrix profile
  
  -----
  `DOI: 10.1109/ICBK.2019.00031     <https://www.cs.ucr.edu/~eamonn/PAN_SKIMP%20%28Matrix%20Profile%20XX%29.pdf>`__
  
  See Table 2
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docstr: Initialize the `stimp` object and compute the Pan Matrix Profile
  
  Parameters
  ----------
  T : numpy.ndarray
      The time series or sequence for which to compute the pan matrix profile
  
  min_m : int, default 3
      The minimum subsequence window size to consider computing a matrix profile
      for
  
  max_m : int, default None
      The maximum subsequence window size to consider computing a matrix profile
      for. When `max_m = None`, this is set to the maximum allowable subsequence
      window size
  
  step : int, default 1
      The step between subsequence window sizes
  
  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.
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code: def __init__(
      self,
      T,
      min_m=3,
      max_m=None,
      step=1,
      device_id=0,
      p=2.0,
  ):
      """
      Initialize the `stimp` object and compute the Pan Matrix Profile
  
      Parameters
      ----------
      T : numpy.ndarray
          The time series or sequence for which to compute the pan matrix profile
  
      min_m : int, default 3
          The minimum subsequence window size to consider computing a matrix profile
          for
  
      max_m : int, default None
          The maximum subsequence window size to consider computing a matrix profile
          for. When `max_m = None`, this is set to the maximum allowable subsequence
          window size
  
      step : int, default 1
          The step between subsequence window sizes
  
      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.
      """
      super().__init__(
          T,
          min_m=min_m,
          max_m=max_m,
          step=step,
          percentage=1.0,
          pre_scraamp=False,
          device_id=device_id,
          mp_func=gpu_aamp,
          p=2.0,
      )
  
imports: from .None import gpu_aamp
  from .aamp_stimp import _aamp_stimp
label_desc: Use image node_id 1 to create a new gpu_aamp_stimp object from inherited base classes: _aamp_stimp with example: obj = gpu_aamp_stimp(T, min_m, max_m, step, device_id, p)