class_docstr: QuantizeConfig for non recurrent Keras layers.
class_docstr_tok: ["QuantizeConfig","for","non","recurrent","Keras","layers","."]
code: def get_config(self):
      # TODO(pulkitb): Add weight and activation quantizer to config.
      # Currently it's created internally, but ideally the quantizers should be
      # part of the constructor and passed in from the registry.
      return {
          "weight_attrs": self.weight_attrs,
          "activation_attrs": self.activation_attrs,
          "quantize_output": self.quantize_output,
          "num_bits_weight": self._num_bits_weight,
          "num_bits_activation": self._num_bits_activation,
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Dict
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_config
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantizers
label_desc: Use image node_id 8 for calling the DefaultNBitQuantizeConfig obj's underlying member method code with example usage: obj.get_config() and returns: dict