parameters: (tensors, side_effects)
return_statement: return result
docstring: Subscribe to a tensor.
  
    This method will attach side effect graphs to a given set
    of tensors. Set of tensors follows from session.run and supports
    single `Tensor`, `list`, nested `list`, `tuple`, `namedtuple`, or `dict`. It
    returns the tensors in the same passed in structure, but as clones with
    side effects applied. The supplied side effect graphs are specified
    as a constructor function which takes the target tensor and
    constructs a side effect graph and returns a list of ops that should
    be control dependencies on fetching the tensor. It will append
    'subscription' to the name scope of the tensor for every node in
    the side effect graph. These control dependencies are what trigger
    the side effects. Subscribe will construct the additions to your
    graph and return the created identity tensor downstream of the control
    dependencies. Use these tensors as you would normally in the rest of
    your tensorflow code. If a given tensor has already been subscribed or a
    tensor returned by a call to subscribe is passed, the previously created
    identity tensor will be reused and the side effect graphs will be added to
    the existing ones.
  
    Args:
      tensors: `Tensor` or set of tensors to subscribe to. Set of tensors format
        follows from `Session.run` and supports single `Tensor`, `list`, nested
        `list`, `tuple`, `namedtuple`, or `dict`.
      side_effects: Function(s) that takes a `Tensor`, construct a subgraph, and
        return a nonempty list of control dependencies. This can be a single
        function or list of functions.
  
    Returns:
      Subscribed tensors, which are identity copies of the passed in tensors
        in the same passed in structure, but the graph has been modified
        such that these are downstream of the control dependencies for
        the side effect graphs. Use these functionally equivalent tensors
        instead of the passed in tensors for further construction or running.
docstring_summary: Subscribe to a tensor.
docstring_tokens: Subscribe to a tensor .
function: def subscribe(tensors, side_effects):
    """Subscribe to a tensor.
  
    This method will attach side effect graphs to a given set
    of tensors. Set of tensors follows from session.run and supports
    single `Tensor`, `list`, nested `list`, `tuple`, `namedtuple`, or `dict`. It
    returns the tensors in the same passed in structure, but as clones with
    side effects applied. The supplied side effect graphs are specified
    as a constructor function which takes the target tensor and
    constructs a side effect graph and returns a list of ops that should
    be control dependencies on fetching the tensor. It will append
    'subscription' to the name scope of the tensor for every node in
    the side effect graph. These control dependencies are what trigger
    the side effects. Subscribe will construct the additions to your
    graph and return the created identity tensor downstream of the control
    dependencies. Use these tensors as you would normally in the rest of
    your tensorflow code. If a given tensor has already been subscribed or a
    tensor returned by a call to subscribe is passed, the previously created
    identity tensor will be reused and the side effect graphs will be added to
    the existing ones.
  
    Args:
      tensors: `Tensor` or set of tensors to subscribe to. Set of tensors format
        follows from `Session.run` and supports single `Tensor`, `list`, nested
        `list`, `tuple`, `namedtuple`, or `dict`.
      side_effects: Function(s) that takes a `Tensor`, construct a subgraph, and
        return a nonempty list of control dependencies. This can be a single
        function or list of functions.
  
    Returns:
      Subscribed tensors, which are identity copies of the passed in tensors
        in the same passed in structure, but the graph has been modified
        such that these are downstream of the control dependencies for
        the side effect graphs. Use these functionally equivalent tensors
        instead of the passed in tensors for further construction or running.
    """
    if not hasattr(side_effects, '__iter__'):
      side_effects = [side_effects]
  
    control_outputs = _ControlOutputCache()
    result = _recursive_apply(
        tensors, lambda t: _scoped_subscribe(t, side_effects, control_outputs))
    return result
function_tokens: def subscribe ( tensors , side_effects ) : if not hasattr ( side_effects , '__iter__' ) : side_effects = [ side_effects ] control_outputs = _ControlOutputCache ( ) result = _recursive_apply ( tensors , lambda t : _scoped_subscribe ( t , side_effects , control_outputs ) ) return result