class_docstr: Set the Rounding and Saturation mode of the output (and accumulator, if applicable)
  of the layers specific in layer list.
  The layer list is empty by default.
  To specify which layer to apply this pass to, perform e.g.:
  hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(layers=['Dense', 'Activation'])
  The Rounding and Saturation modes are 'None' by default (so use the compiler defaults)
  To set which mode to use:
  hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(rounding_mode='AP_RND_CONV')
  hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(saturation_mode='AP_SAT')
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code: def match(self, node):
      layer_match = (
          node.class_name in self.layers or node.name in self.layers
      )
      t = str(node.get_output_variable().type.precision)
      # check that the type doesn't already contain the rounding mode
      rs_match = False
      if self.rounding_mode is not None:
          rs_match = rs_match or not (self.rounding_mode in t)
      if self.saturation_mode is not None:
          rs_match = rs_match or not (self.saturation_mode in t)
      return layer_match and rs_match
  
imports: import numpy
  import tensorflow
  from hls4ml.model.layers import BatchNormalization, register_layer
  from hls4ml.model.optimizer import ConfigurableOptimizerPass, OptimizerPass, register_pass
  from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType, QKerasPO2Quantizer
label_desc: Use image node_id 2 for calling the OutputRoundingSaturationMode obj's underlying member method code with example usage: obj.match(node) and returns: unknown