class_docstr: QuantizeConfig for RNN layers.
class_docstr_tok: ["QuantizeConfig","for","RNN","layers","."]
code: def get_activations_and_quantizers(self, layer):
      activations_quantizers = []
      for activation_attrs_cell, rnn_cell in zip(
          self.activation_attrs, self._get_rnn_cells(layer)
      ):
          for activation_attr in activation_attrs_cell:
              activations_quantizers.append(
                  (
                      getattr(rnn_cell, activation_attr),
                      self.activation_quantizer,
                  )
              )
  
      return activations_quantizers
  
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 2 for calling the Default8BitQuantizeConfigRNN obj's underlying member method code with example usage: obj.get_activations_and_quantizers(layer) and returns: activations_quantizers