class_docstr: Convolutional (FIR) reverb.
class_docstr_tok: ["Convolutional","(","FIR",")","reverb","."]
docstr: Takes neural network outputs directly as the impulse response.
  
  Args:
    trainable: Learn the impulse_response as a single variable for the entire
      dataset.
    reverb_length: Length of the impulse response. Only used if
      trainable=True.
    add_dry: Add dry signal to reverberated signal on output.
    name: Name of processor module.
docstr_tok: ["Takes","neural","network","outputs","directly","as","the","impulse","response",".","Args",":","trainable",":","Learn","the","impulse_response","as","a","single","variable","for","the","entire","dataset",".","reverb_length",":","Length","of","the","impulse","response",".","Only","used","if","trainable=True",".","add_dry",":","Add","dry","signal","to","reverberated","signal","on","output",".","name",":","Name","of","processor","module","."]
code: def __init__(
      self,
      trainable=False,
      reverb_length=48000,
      add_dry=True,
      name="reverb",
  ):
      """Takes neural network outputs directly as the impulse response.
  
      Args:
        trainable: Learn the impulse_response as a single variable for the entire
          dataset.
        reverb_length: Length of the impulse response. Only used if
          trainable=True.
        add_dry: Add dry signal to reverberated signal on output.
        name: Name of processor module.
      """
      super().__init__(name=name, trainable=trainable)
      self._reverb_length = reverb_length
      self._add_dry = add_dry
  
imports: from ddsp import core
  from ddsp import processors
  from ddsp import synths
  import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new Reverb object from inherited base classes: processors with example: obj = Reverb(trainable, reverb_length, add_dry, name)