class_docstr: The TFE Converter.
  
  Args:
      config: `Config` to use when constructing the TFE graph.
          Defaults to the current global TFE config.
      protocol: `Protocol` to use when constructing the TFE graph.
          Defaults to the current global TFE protocol.
class_docstr_tok: ["The","TFE","Converter",".","Args",":","config",":","`","Config","`","to","use","when","constructing","the","TFE","graph",".","Defaults","to","the","current","global","TFE","config",".","protocol",":","`","Protocol","`","to","use","when","constructing","the","TFE","graph",".","Defaults","to","the","current","global","TFE","protocol","."]
code: def __init__(
      self,
      config: Optional[Config] = None,
      protocol: Optional[Protocol] = None,
  ) -> None:
      self.config = config if config is not None else get_config()
      if protocol is not None:
          tfe.set_protocol(protocol)
      self.initializers = {}
      self.trainable_weights = []
      self.node_op = {}
  
imports: import functools
  from typing import Any
  from typing import List
  from typing import Optional
  from typing import Union
  import numpy
  import onnx
  import tensorflow
  import tf2onnx
  from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
  from onnx.onnx_ml_pb2 import ModelProto
  from tf2onnx import optimizer
  import tf_encrypted
  from tf_encrypted.config import Config
  from tf_encrypted.config import get_config
  from tf_encrypted.keras import optimizers
  from tf_encrypted.keras.models import BaseModel
  from tf_encrypted.player import Player
  from tf_encrypted.protocol import Protocol
  from tf_encrypted.protocol.protocol import TFETensor
  from .nodes import build_node
label_desc: Use image node_id 1 to create a new Converter object  with example: obj = Converter(config, protocol)