class_docstr: Abstract base class for signal processors.
  
  Since most effects / synths require specificly formatted control signals
  (such as amplitudes and frequenices), each processor implements a
  get_controls(inputs) method, where inputs are a variable number of tensor
  arguments that are typically neural network outputs. Check each child class
  for the class-specific arguments it expects. This gives a dictionary of
  controls that can then be passed to get_signal(controls). The
  get_outputs(inputs) method calls both in succession and returns a nested
  output dictionary with all controls and signals.
class_docstr_tok: ["Abstract","base","class","for","signal","processors",".","Since","most","effects","\/","synths","require","specificly","formatted","control","signals","(","such","as","amplitudes","and","frequenices",")",",","each","processor","implements","a","get_controls","(","inputs",")","method",",","where","inputs","are","a","variable","number","of","tensor","arguments","that","are","typically","neural","network","outputs",".","Check","each","child","class","for","the","class-specific","arguments","it","expects",".","This","gives","a","dictionary","of","controls","that","can","then","be","passed","to","get_signal","(","controls",")",".","The","get_outputs","(","inputs",")","method","calls","both","in","succession","and","returns","a","nested","output","dictionary","with","all","controls","and","signals","."]
code: def __init__(self, name: Text, trainable: bool = False):
      super().__init__(name=name, trainable=trainable, autocast=False)
  
imports: from typing import Dict, Text
  from ddsp import core
  from ddsp import dags
  import gin
  import tensorflow
label_desc: Use image node_id 1 to create a new Processor object from inherited base classes: tfkl with example: obj = Processor(name, trainable)