parameters: (self, inside_ops=(), passthrough_ts=())
docstring: Create a subgraph containing the given ops and the "passthrough" tensors.
  
      Args:
        inside_ops: an object convertible to a list of `tf.Operation`. This list
          defines all the operations in the subgraph.
        passthrough_ts: an object convertible to a list of `tf.Tensor`. This list
          define all the "passthrough" tensors. A passthrough tensor is a tensor
          which goes directly from the input of the subgraph to it output, without
          any intermediate operations. All the non passthrough tensors are
          silently ignored.
      Raises:
        TypeError: if inside_ops cannot be converted to a list of `tf.Operation`
          or if `passthrough_ts` cannot be converted to a list of `tf.Tensor`.
docstring_summary: Create a subgraph containing the given ops and the "passthrough" tensors.
docstring_tokens: Create a subgraph containing the given ops and the passthrough tensors .
function: def __init__(self, inside_ops=(), passthrough_ts=()):
      """Create a subgraph containing the given ops and the "passthrough" tensors.
  
      Args:
        inside_ops: an object convertible to a list of `tf.Operation`. This list
          defines all the operations in the subgraph.
        passthrough_ts: an object convertible to a list of `tf.Tensor`. This list
          define all the "passthrough" tensors. A passthrough tensor is a tensor
          which goes directly from the input of the subgraph to it output, without
          any intermediate operations. All the non passthrough tensors are
          silently ignored.
      Raises:
        TypeError: if inside_ops cannot be converted to a list of `tf.Operation`
          or if `passthrough_ts` cannot be converted to a list of `tf.Tensor`.
      """
  
      inside_ops = util.make_list_of_op(inside_ops)
      passthrough_ts = util.make_list_of_t(passthrough_ts)
      ops_and_ts = inside_ops + passthrough_ts
      if ops_and_ts:
        self._graph = util.get_unique_graph(ops_and_ts)
        self._ops = inside_ops
  
        # Compute inside and outside tensor
        inputs, outputs, insides = select.compute_boundary_ts(inside_ops)
  
        # Compute passthrough tensors, silently ignoring the non-passthrough ones.
        all_tensors = frozenset(inputs + outputs + list(insides))
        self._passthrough_ts = [t for t in passthrough_ts if t not in all_tensors]
  
        # Set inputs and outputs.
        self._input_ts = inputs + self._passthrough_ts
        self._output_ts = outputs + self._passthrough_ts
      else:
        self._graph = None
        self._passthrough_ts = []
        self._input_ts = []
        self._output_ts = []
        self._ops = []
function_tokens: def __init__ ( self , inside_ops = ( ) , passthrough_ts = ( ) ) : inside_ops = util . make_list_of_op ( inside_ops ) passthrough_ts = util . make_list_of_t ( passthrough_ts ) ops_and_ts = inside_ops + passthrough_ts if ops_and_ts : self . _graph = util . get_unique_graph ( ops_and_ts ) self . _ops = inside_ops # Compute inside and outside tensor inputs , outputs , insides = select . compute_boundary_ts ( inside_ops ) # Compute passthrough tensors, silently ignoring the non-passthrough ones. all_tensors = frozenset ( inputs + outputs + list ( insides ) ) self . _passthrough_ts = [ t for t in passthrough_ts if t not in all_tensors ] # Set inputs and outputs. self . _input_ts = inputs + self . _passthrough_ts self . _output_ts = outputs + self . _passthrough_ts else : self . _graph = None self . _passthrough_ts = [ ] self . _input_ts = [ ] self . _output_ts = [ ] self . _ops = [ ]