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","."]
code: def __init__(self, name=None):
      self.name = name if name else ""
      self._current_graph = [{}]
  
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 1 to create a new TranscriptionContext object  with example: obj = TranscriptionContext(name)