class_docstr: QuantizeConfig for RNN layers.
class_docstr_tok: ["QuantizeConfig","for","RNN","layers","."]
code: def _flatten(self, list_of_lists):
      flat_list = [
          item for sublist in list_of_lists for item in sublist
      ]
      return flat_list
  
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 3 for calling the DefaultNBitQuantizeConfigRNN obj's underlying member method code with example usage: obj._flatten(list_of_lists) and returns: flat_list