class_docstr: The Activations class extends a numpy ndarray with a frame rate (fps)
  attribute.
  
  Parameters
  ----------
  data : str, file handle or numpy array
      Either file name/handle to read the data from or array.
  fps : float, optional
      Frames per second (must be set if `data` is given as an array).
  sep : str, optional
      Separator between activation values (if read from file).
  dtype : numpy dtype
      Data-type the activations are stored/saved/kept.
  
  Attributes
  ----------
  fps : float
      Frames per second.
  
  Notes
  -----
  If a filename or file handle is given, an undefined or empty separator
  means that the file should be treated as a numpy binary file.
  Only binary files can store the frame rate of the activations.
  Text files should not be used for anything else but manual inspection
  or I/O with other programs.
docstr: Load the activations from a file.
  
  Parameters
  ----------
  infile : str or file handle
      Input file name or file handle.
  fps : float, optional
      Frames per second; if set, it overwrites the saved frame rate.
  sep : str, optional
      Separator between activation values.
  
  Returns
  -------
  :class:`Activations` instance
      :class:`Activations` instance.
  
  Notes
  -----
  An undefined or empty separator means that the file should be treated
  as a numpy binary file.
  Only binary files can store the frame rate of the activations.
  Text files should not be used for anything else but manual inspection
  or I/O with other programs.
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code: def load(cls, infile, fps=None, sep=None):
      """
      Load the activations from a file.
  
      Parameters
      ----------
      infile : str or file handle
          Input file name or file handle.
      fps : float, optional
          Frames per second; if set, it overwrites the saved frame rate.
      sep : str, optional
          Separator between activation values.
  
      Returns
      -------
      :class:`Activations` instance
          :class:`Activations` instance.
  
      Notes
      -----
      An undefined or empty separator means that the file should be treated
      as a numpy binary file.
      Only binary files can store the frame rate of the activations.
      Text files should not be used for anything else but manual inspection
      or I/O with other programs.
  
      """
      # load the activations
      if sep in [None, ""]:
          # numpy binary format
          data = np.load(infile)
          if isinstance(data, np.lib.npyio.NpzFile):
              # .npz file, extract the frame rate if given
              if "fps" in data.files:
                  try:
                      fps = float(data["fps"])
                  except ValueError:
                      fps = None
              # and overwrite the data
              data = data["activations"]
      else:
          # simple text format
          data = np.loadtxt(infile, delimiter=sep)
      if data.ndim > 1 and data.shape[1] == 1:
          # flatten the array if it has only 1 real dimension
          data = data.flatten()
      # instantiate a new object
      return cls(data, fps)
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  from ..processors import Processor
  from .None import beats, chords, downbeats, key, notes, onsets, tempo
  from .beats import BeatDetectionProcessor, BeatTrackingProcessor, CRFBeatDetectionProcessor, DBNBeatTrackingProcessor, MultiModelSelectionProcessor, RNNBeatProcessor
  from .chords import CNNChordFeatureProcessor, CRFChordRecognitionProcessor, DeepChromaChordRecognitionProcessor
  from .downbeats import RNNDownBeatProcessor, DBNDownBeatTrackingProcessor, PatternTrackingProcessor, RNNBarProcessor, DBNBarTrackingProcessor
  from .key import CNNKeyRecognitionProcessor
  from .notes import CNNPianoNoteProcessor, ADSRNoteTrackingProcessor, NoteOnsetPeakPickingProcessor, NotePeakPickingProcessor, RNNPianoNoteProcessor
  from .onsets import CNNOnsetProcessor, OnsetPeakPickingProcessor, RNNOnsetProcessor, SpectralOnsetProcessor
  from .tempo import TempoEstimationProcessor
label_desc: Use image node_id 4 for calling the Activations obj's underlying member method code with example usage: obj.load(cls, infile, fps, sep) and returns: cls