class_docstr: QuantizeConfig for RNN layers.
class_docstr_tok: ["QuantizeConfig","for","RNN","layers","."]
code: def _flatten(self, list_of_lists):
      flat_list = []
      for sublist in list_of_lists:
          for item in sublist:
              flat_list.append(item)
      return flat_list
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  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.default_8bit import default_8bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantizers
label_desc: Use image node_id 3 for calling the Default8BitQuantizeConfigRNN obj's underlying member method code with example usage: obj._flatten(list_of_lists) and returns: flat_list