class_docstr: Import tensorflow model to nnp model.
      
class_docstr_tok: ["Import","tensorflow","model","to","nnp","model","."]
code: def convert_to_onnx(self, graph_def, inputs, outputs):
      with tf.Graph().as_default() as tf_graph:
          tf.import_graph_def(graph_def, name="")
      with tf_loader.tf_session(graph=tf_graph):
          g = process_tf_graph(
              tf_graph,
              continue_on_error=False,
              target=",".join(constants.DEFAULT_TARGET),
              opset=11,
              input_names=inputs,
              output_names=outputs,
              inputs_as_nchw=None,
          )
      onnx_graph = optimizer.optimize_graph(g)
      model_proto = onnx_graph.make_model(
          "converted from {}".format(self._tf_file)
      )
      return model_proto
  
file_path: nnabla/python/src/nnabla/utils/converter/tensorflow/importer.py
imports: import os
  import tensorflow
  import tflite
  import tflite2onnx
  from tensorflow.core.framework import graph_pb2
  from tensorflow.python.tools import freeze_graph
  from tf2onnx import constants, tf_loader, optimizer
  from tf2onnx.tfonnx import process_tf_graph
  from .common import find_out_terminal_node
  from ..onnx import OnnxImporter
label_desc: Use image node_id 2 for calling the TensorflowImporter obj's underlying member method code with example usage: obj.convert_to_onnx(graph_def, inputs, outputs) and returns: model_proto