class_docstr: Class for RNN performance generation models.
docstr: Evaluate the log likelihood of a performance.
  
  Args:
    sequence: The Performance object for which to evaluate the log likelihood.
    control_values: List of (single) values for all control signal.
    disable_conditioning: Whether or not to disable optional conditioning. If
        True, disable conditioning. If False, do not disable. None when no
        conditioning or it is not optional.
  
  
  Returns:
    The log likelihood of `sequence` under this model.
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code: def performance_log_likelihood(
      self, sequence, control_values, disable_conditioning
  ):
      """Evaluate the log likelihood of a performance.
  
      Args:
        sequence: The Performance object for which to evaluate the log likelihood.
        control_values: List of (single) values for all control signal.
        disable_conditioning: Whether or not to disable optional conditioning. If
            True, disable conditioning. If False, do not disable. None when no
            conditioning or it is not optional.
  
  
      Returns:
        The log likelihood of `sequence` under this model.
      """
      if control_values:
          control_event = tuple(control_values)
          if disable_conditioning is not None:
              control_event = (disable_conditioning, control_event)
          control_events = [control_event] * len(sequence)
      else:
          control_events = None
  
      return self._evaluate_log_likelihood(
          [sequence], control_events=control_events
      )[0]
  
file_path: magenta/magenta/models/performance_rnn/performance_model.py
imports: import collections
  import functools
  from magenta.contrib import training
  from magenta.models.shared import events_rnn_model
  import note_seq
  from note_seq.protobuf import generator_pb2
label_desc: Use image node_id 2 for calling the PerformanceRnnModel obj's underlying member method code with example usage: obj.performance_log_likelihood(sequence, control_values, disable_conditioning) and returns: self