(
      self, num_steps, primer_sequence, temperature=1.0, beam_size=1,
      branch_factor=1, steps_per_iteration=1, control_signal_fns=None,
      disable_conditioning_fn=None)
return self._generate_events(
        num_steps, primer_sequence, temperature, beam_size, branch_factor,
        steps_per_iteration, control_events=control_events,
        control_state=control_state,
        extend_control_events_callback=extend_control_events_callback)
Generate a performance track from a primer performance track.

    Args:
      num_steps: The integer length in steps of the final track, after
          generation. Includes the primer.
      primer_sequence: The primer sequence, a Performance object.
      temperature: A float specifying how much to divide the logits by
         before computing the softmax. Greater than 1.0 makes tracks more
         random, less than 1.0 makes tracks less random.
      beam_size: An integer, beam size to use when generating tracks via
          beam search.
      branch_factor: An integer, beam search branch factor to use.
      steps_per_iteration: An integer, number of steps to take per beam search
          iteration.
      control_signal_fns: A list of functions that map time step to desired
          control value, or None if not using control signals.
      disable_conditioning_fn: A function that maps time step to whether or not
          conditioning should be disabled, or None if there is no conditioning
          or conditioning is not optional.

    Returns:
      The generated Performance object (which begins with the provided primer
      track).
Generate a performance track from a primer performance track.
Generate a performance track from a primer performance track .
def generate_performance(
      self, num_steps, primer_sequence, temperature=1.0, beam_size=1,
      branch_factor=1, steps_per_iteration=1, control_signal_fns=None,
      disable_conditioning_fn=None):
    """Generate a performance track from a primer performance track.

    Args:
      num_steps: The integer length in steps of the final track, after
          generation. Includes the primer.
      primer_sequence: The primer sequence, a Performance object.
      temperature: A float specifying how much to divide the logits by
         before computing the softmax. Greater than 1.0 makes tracks more
         random, less than 1.0 makes tracks less random.
      beam_size: An integer, beam size to use when generating tracks via
          beam search.
      branch_factor: An integer, beam search branch factor to use.
      steps_per_iteration: An integer, number of steps to take per beam search
          iteration.
      control_signal_fns: A list of functions that map time step to desired
          control value, or None if not using control signals.
      disable_conditioning_fn: A function that maps time step to whether or not
          conditioning should be disabled, or None if there is no conditioning
          or conditioning is not optional.

    Returns:
      The generated Performance object (which begins with the provided primer
      track).
    """
    if control_signal_fns:
      control_event = tuple(f(0) for f in control_signal_fns)
      if disable_conditioning_fn is not None:
        control_event = (disable_conditioning_fn(0), control_event)
      control_events = [control_event]
      control_state = PerformanceControlState(
          current_perf_index=0, current_perf_step=0)
      extend_control_events_callback = functools.partial(
          _extend_control_events, control_signal_fns, disable_conditioning_fn)
    else:
      control_events = None
      control_state = None
      extend_control_events_callback = None

    return self._generate_events(
        num_steps, primer_sequence, temperature, beam_size, branch_factor,
        steps_per_iteration, control_events=control_events,
        control_state=control_state,
        extend_control_events_callback=extend_control_events_callback)
def generate_performance ( self , num_steps , primer_sequence , temperature = 1.0 , beam_size = 1 , branch_factor = 1 , steps_per_iteration = 1 , control_signal_fns = None , disable_conditioning_fn = None ) : if control_signal_fns : control_event = tuple ( f ( 0 ) for f in control_signal_fns ) if disable_conditioning_fn is not None : control_event = ( disable_conditioning_fn ( 0 ) , control_event ) control_events = [ control_event ] control_state = PerformanceControlState ( current_perf_index = 0 , current_perf_step = 0 ) extend_control_events_callback = functools . partial ( _extend_control_events , control_signal_fns , disable_conditioning_fn ) else : control_events = None control_state = None extend_control_events_callback = None return self . _generate_events ( num_steps , primer_sequence , temperature , beam_size , branch_factor , steps_per_iteration , control_events = control_events , control_state = control_state , extend_control_events_callback = extend_control_events_callback )