class_docstr: Class for RNN drum track generation models.
docstr: Generate a drum track from a primer drum track.
  
  Args:
    num_steps: The integer length in steps of the final drum track, after
        generation. Includes the primer.
    primer_drums: The primer drum track, a DrumTrack object.
    temperature: A float specifying how much to divide the logits by
       before computing the softmax. Greater than 1.0 makes drum tracks more
       random, less than 1.0 makes drum tracks less random.
    beam_size: An integer, beam size to use when generating drum 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.
  
  Returns:
    The generated DrumTrack object (which begins with the provided primer drum
        track).
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code: def generate_drum_track(
      self,
      num_steps,
      primer_drums,
      temperature=1.0,
      beam_size=1,
      branch_factor=1,
      steps_per_iteration=1,
  ):
      """Generate a drum track from a primer drum track.
  
      Args:
        num_steps: The integer length in steps of the final drum track, after
            generation. Includes the primer.
        primer_drums: The primer drum track, a DrumTrack object.
        temperature: A float specifying how much to divide the logits by
           before computing the softmax. Greater than 1.0 makes drum tracks more
           random, less than 1.0 makes drum tracks less random.
        beam_size: An integer, beam size to use when generating drum 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.
  
      Returns:
        The generated DrumTrack object (which begins with the provided primer drum
            track).
      """
      return self._generate_events(
          num_steps,
          primer_drums,
          temperature,
          beam_size,
          branch_factor,
          steps_per_iteration,
      )
  
file_path: magenta/magenta/models/drums_rnn/drums_rnn_model.py
imports: 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 1 for calling the DrumsRnnModel obj's underlying member method code with example usage: obj.generate_drum_track(num_steps, primer_drums, temperature, beam_size, branch_factor, steps_per_iteration) and returns: self