class_docstr: Emulate quantization of tensors passed through the layer.
code: def call(self, inputs, training=None):
      if not self.quantizer:
          return inputs
  
      if training is None:
          training = tf.keras.backend.learning_phase()
  
      def _make_quantizer_fn(train_var):
          def quantizer_fn():
              return self.quantizer(
                  inputs, train_var, weights=self.quantizer_vars
              )
  
          return quantizer_fn
  
      return utils.smart_cond(
          training, _make_quantizer_fn(True), _make_quantizer_fn(False)
      )
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/quantize_layer.py
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 quantizers
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 3 for calling the QuantizeLayer obj's underlying member method code with example usage: obj.call(inputs, training) and returns: utils, inputs, quantizer_fn, self