class_docstr: Parameterize impulse response as a simple exponential decay.
class_docstr_tok: ["Parameterize","impulse","response","as","a","simple","exponential","decay","."]
docstr: Constructor.
  
  Args:
    trainable: Learn the impulse_response as a single variable for the entire
      dataset.
    reverb_length: Length of the impulse response.
    scale_fn: Function by which to scale the network outputs.
    add_dry: Add dry signal to reverberated signal on output.
    name: Name of processor module.
docstr_tok: ["Constructor",".","Args",":","trainable",":","Learn","the","impulse_response","as","a","single","variable","for","the","entire","dataset",".","reverb_length",":","Length","of","the","impulse","response",".","scale_fn",":","Function","by","which","to","scale","the","network","outputs",".","add_dry",":","Add","dry","signal","to","reverberated","signal","on","output",".","name",":","Name","of","processor","module","."]
code: def __init__(
      self,
      trainable=False,
      reverb_length=48000,
      scale_fn=core.exp_sigmoid,
      add_dry=True,
      name="exp_decay_reverb",
  ):
      """Constructor.
  
      Args:
        trainable: Learn the impulse_response as a single variable for the entire
          dataset.
        reverb_length: Length of the impulse response.
        scale_fn: Function by which to scale the network outputs.
        add_dry: Add dry signal to reverberated signal on output.
        name: Name of processor module.
      """
      super().__init__(name=name, add_dry=add_dry, trainable=trainable)
      self._reverb_length = reverb_length
      self._scale_fn = scale_fn
  
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 ExpDecayReverb object from inherited base classes: Reverb with example: obj = ExpDecayReverb(trainable, reverb_length, scale_fn, add_dry, name)