code: def convert(self):
      prog = Program()
      if len(self.graph_stack) == 0:
          raise ValueError("At least one TF function must be present")
      if self.graph_stack[0] != "main":
          msg = "TF root graph must be named 'main'. Got {}"
          raise ValueError(msg.format(self.graph_stack[0]))
      graph = self.tfssa.functions["main"].graph
      for g_name in self.graph_stack[1:]:
          self.context.add_graph(
              g_name, self.tfssa.functions[g_name].graph
          )
      self.convert_main_graph(prog, graph)
      return prog
  
imports: from coremltools import _logger
  from coremltools.converters._profile_utils import _profile
  from coremltools.converters.mil._deployment_compatibility import AvailableTarget
  from coremltools.converters.mil.input_types import ImageType, InputType, RangeDim
  from coremltools.converters.mil.input_types import Shape
  from coremltools.converters.mil.input_types import TensorType, _get_shaping_class
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program, get_new_symbol, types
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic
  from coremltools.converters.mil.mil.var import Var
  from .._utils import get_output_names
  from .basic_graph_ops import simple_topsort
  from .convert_utils import convert_graph
label_desc: Use image node_id 9 for calling the TFConverter obj's underlying member method code with example usage: obj.convert() and returns: prog