class_docstr: Maintains a map from torch operations to their MIL values
  while building the graph. Can be used to process subgraphs recursively
  by pushing new context when stepping into a subgraph and popping that
  context when stepping out.
class_docstr_tok: ["Maintains","a","map","from","torch","operations","to","their","MIL","values","while","building","the","graph",".","Can","be","used","to","process","subgraphs","recursively","by","pushing","new","context","when","stepping","into","a","subgraph","and","popping","that","context","when","stepping","out","."]
docstr: Arguments:
      ssa_var: Varable to add to the graph being constructed.
      torch_name: Optional unique string identifier of the operation. If
          ommitted, it will use @ssa_var.name.
docstr_tok: ["Arguments",":","ssa_var",":","Varable","to","add","to","the","graph","being","constructed",".","torch_name",":","Optional","unique","string","identifier","of","the","operation",".","If","ommitted",",","it","will","use","@","ssa_var.name","."]
code: def add(self, ssa_var, torch_name=None):
      """
      Arguments:
          ssa_var: Varable to add to the graph being constructed.
          torch_name: Optional unique string identifier of the operation. If
              ommitted, it will use @ssa_var.name.
      """
      if torch_name is None:
          torch_name = ssa_var.name
      if torch_name in self._current_graph[-1]:
          print("Torch var {} is added again.".format(torch_name))
          return
      self._current_graph[-1][torch_name] = ssa_var
  
imports: from __future__ import print_function
  from six import string_types
  import logging
  import torch
  from coremltools.converters.mil.input_types import InputType, ImageType
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Placeholder, Function, Program, get_new_symbol
  from coremltools.converters.mil.mil import Var
  from .internal_graph import *
  from .ops import *
  from .torch_op_registry import _TORCH_OPS_REGISTRY
  from .torchir_passes import *
label_desc: Use image node_id 2 for calling the TranscriptionContext obj's underlying member method code with example usage: obj.add(ssa_var, torch_name) without return types