class_docstr: Tempo evaluation class.
  
  Parameters
  ----------
  detections : str, list of tuples or numpy array
      Detected tempi (rows) and their strengths (columns).
      If a file name is given, load them from this file.
  annotations : str, list or numpy array
      Annotated ground truth tempi (rows) and their strengths (columns).
      If a file name is given, load them from this file.
  tolerance : float, optional
      Evaluation tolerance (max. allowed deviation).
  double : bool, optional
      Include double and half tempo variations.
  triple : bool, optional
      Include triple and third tempo variations.
  sort : bool, optional
      Sort the tempi by their strengths (descending order).
  max_len : bool, optional
      Evaluate at most `max_len` tempi.
  name : str, optional
      Name of the evaluation to be displayed.
  
  Notes
  -----
  For P-Score, the number of detected tempi will be limited to the number
  of annotations (if not further limited by `max_len`).
  For Accuracy 1 & 2 only one detected tempo is used. Depending on `sort`,
  this can be either the first or the strongest one.
code: def __len__(self):
      return 1
  
imports: from __future__ import absolute_import, division, print_function
  import warnings
  import numpy
  from .None import EvaluationMixin, MeanEvaluation, evaluation_io
  from ..io import load_tempo
label_desc: Use image node_id 2 for calling the TempoEvaluation obj's underlying member method code with example usage: obj.__len__() and returns: int