class_docstr: Activation wrapper for quantization aware training.
  
  The goal of this class is to apply quantize operations during training such
  that the training network mimics quantization loss experienced in activations
  during inference.
  
  It introduces quantization loss before and after activations as required to
  mimic inference loss. The layer has built-in knowledge of how quantized
  activations are laid out during inference to emulate exact behavior.
  
  For example, ReLU activations are typically fused into their parent layer
  such as Conv/Dense. Hence, loss is introduced only after the activation has
  been applied. For Softmax on the other hand quantization loss is experienced
  both before and after the activation.
  
  Input shape:
    Arbitrary.
  
  Output shape:
    Same shape as input.
class_docstr_tok: ["Activation","wrapper","for","quantization","aware","training",".","The","goal","of","this","class","is","to","apply","quantize","operations","during","training","such","that","the","training","network","mimics","quantization","loss","experienced","in","activations","during","inference",".","It","introduces","quantization","loss","before","and","after","activations","as","required","to","mimic","inference","loss",".","The","layer","has","built-in","knowledge","of","how","quantized","activations","are","laid","out","during","inference","to","emulate","exact","behavior",".","For","example",",","ReLU","activations","are","typically","fused","into","their","parent","layer","such","as","Conv\/Dense",".","Hence",",","loss","is","introduced","only","after","the","activation","has","been","applied",".","For","Softmax","on","the","other","hand","quantization","loss","is","experienced","both","before","and","after","the","activation",".","Input","shape",":","Arbitrary",".","Output","shape",":","Same","shape","as","input","."]
code: def _is_supported_activation(self, activation):
      activation_name = self._name(activation)
  
      return (
          activation_name in self._PRE_QUANT_ACTIVATIONS
          or activation_name in self._POST_QUANT_ACTIVATIONS
          or activation_name in self._NO_QUANTIZE_ACTIVATIONS
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 3 for calling the QuantizeAwareActivation obj's underlying member method code with example usage: obj._is_supported_activation(activation) and returns: unknown