class_docstr: Quantizes the weights and activations of the keras layer it wraps.
class_docstr_tok: ["Quantizes","the","weights","and","activations","of","the","keras","layer","it","wraps","."]
docstr: Use currying to return True/False specialized fns to the cond.
docstr_tok: ["Use","currying","to","return","True\/False","specialized","fns","to","the","cond","."]
code: def _make_quantizer_fn(self, quantizer, x, training, quantizer_vars):
      """Use currying to return True/False specialized fns to the cond."""
  
      def quantizer_fn():
          return quantizer(x, training, weights=quantizer_vars)
  
      return quantizer_fn
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.util import tf_inspect
  from tensorflow_model_optimization.python.core.keras import metrics
  from tensorflow_model_optimization.python.core.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 6 for calling the QuantizeWrapper obj's underlying member method code with example usage: obj._make_quantizer_fn(quantizer, x, training, quantizer_vars) and returns: quantizer_fn, quantizer