code: def emit_RNNs(self, IR_node, func):
      assert False
      # for Keras
      if "dropout" in IR_node.IR_layer.attr:
          dropout_str = ",dropout = {}, recurrent_dropout = {}".format(
              IR_node.IR_layer.attr["dropout"].f,
              IR_node.IR_layer.attr["recurrent_dropout"].f,
          )
      else:
          dropout_str = ""
  
      code = "{:<15} = {}(units = {}, use_bias = {} {})({})".format(
          IR_node.name,
          func,
          IR_node.IR_layer.attr["units"].i,
          IR_node.IR_layer.attr["use_bias"].b,
          dropout_str,
          IR_node.in_edges[0],
      )
  
      return code
  
imports: from __future__ import division
  import os
  import numpy
  from six import string_types
  from mmdnn.conversion.common.IR.IR_graph import IRGraph, IRGraphNode
  import mmdnn.conversion.common.IR.graph_pb2
  from mmdnn.conversion.common.IR.graph_pb2 import NodeDef, GraphDef, DataType
  from mmdnn.conversion.common.DataStructure.emitter import Emitter
  from mmdnn.conversion.common.utils import *
  from mmdnn.conversion.coreml.coreml_utils import _infer_coreml_input_shape
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models import datatypes
  from coremltools.models import MLModel
  from coremltools.models.utils import save_spec
label_desc: Use image node_id 36 for calling the CoreMLEmitter obj's underlying member method code with example usage: obj.emit_RNNs(IR_node, func) and returns: code