docstr: ssa_vars: list[Var] / tuple[Var] (multiple outputs) or
  Var (single_output)
  is_new_var: True if ssa_vars are newly created for tf_name.
docstr_tok: ["ssa_vars",":","list","[","Var","]","\/","tuple","[","Var","]","(","multiple","outputs",")","or","Var","(","single_output",")","is_new_var",":","True","if","ssa_vars","are","newly","created","for","tf_name","."]
code: def add(self, tf_name, ssa_vars, is_new_var=True):
      """
      ssa_vars: list[Var] / tuple[Var] (multiple outputs) or
      Var (single_output)
      is_new_var: True if ssa_vars are newly created for tf_name.
      """
      if tf_name in self.context:
          # Overriding allow us to translate while_loop body twice (which is
          # needed to figure out shapes changes during iterates)
          msg = "TF var %s is added again. Overriding previous value"
          logging.info(msg % tf_name)
      if (
          is_new_var
          and isinstance(ssa_vars, Var)
          and tf_name != ssa_vars.name
      ):
          msg = (
              "MIL op's name ({}) does not match TensorFlow's node name ({})."
              " Warning: Node added to context must have the same name as the name passed to context."
          )
          raise ValueError(msg.format(tf_name, ssa_vars.name))
      self.context[tf_name] = ssa_vars
  
imports: import six
  import logging
  from coremltools.converters.mil.input_types import InputType, TensorType, ImageType, RangeDim, _get_shaping_class
  from coremltools.converters.mil.input_types import Shape
  from coremltools.converters.mil.mil.var import Var
  from coremltools.converters.mil.mil import get_new_symbol
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic
  from coremltools.converters.mil.mil import types
  from .basic_graph_ops import topsort, simple_topsort
  from .convert_utils import convert_graph
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Program
  from coremltools.converters.mil.mil import Function
  from .ssa_passes.tf_passes import tensorflow_passes
  from coremltools.converters._profile_utils import _profile
label_desc: Use image node_id 2 for calling the TranscriptionContext obj's underlying member method code with example usage: obj.add(tf_name, ssa_vars, is_new_var) without return types