Function tuple

tensorflow/python/ops/control_flow_ops.py:2948–3014

Group tensors together. This creates a tuple of tensors with the same values as the `tensors` argument, except that the value of each tensor is only returned after the values of all tensors have been computed. `control_inputs` contains additional ops that have to finish before this op fi

(tensors, name=None, control_inputs=None)

@tf_export(v1=["tuple"])
def tuple(tensors, name=None, control_inputs=None):  # pylint: disable=redefined-builtin
  """Group tensors together.

  This creates a tuple of tensors with the same values as the `tensors`
  argument, except that the value of each tensor is only returned after the
  values of all tensors have been computed.

  `control_inputs` contains additional ops that have to finish before this op
  finishes, but whose outputs are not returned.

  This can be used as a "join" mechanism for parallel computations: all the
  argument tensors can be computed in parallel, but the values of any tensor
  returned by `tuple` are only available after all the parallel computations
  are done.

  See also `tf.group` and
  `tf.control_dependencies`.

  Args:
    tensors: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`.
    name: (optional) A name to use as a `name_scope` for the operation.
    control_inputs: List of additional ops to finish before returning.

  Returns:
    Same as `tensors`.

  Raises:
    ValueError: If `tensors` does not contain any `Tensor` or `IndexedSlices`.
    TypeError: If `control_inputs` is not a list of `Operation` or `Tensor`
      objects.

  """
  if context.executing_eagerly():
    return tensors
  with ops.name_scope(name, "tuple", tensors) as name:
    tensors = [
        t if (isinstance(t, ops.Operation) or tensor_util.is_tensor(t) or
              t is None) else ops.convert_to_tensor(t) for t in tensors
    ]
    gating_ops = [
        t if isinstance(t, ops.Operation) else t.op
        for t in tensors
        if t is not None
    ]
    if control_inputs:
      for c in control_inputs:
        if isinstance(c, ops.Tensor):
          c = c.op
        elif not isinstance(c, ops.Operation):
          raise TypeError("Control input must be Operation or Tensor: %s" % c)
        gating_ops.append(c)
    # Note that in order to ensure ordering in the pbtxt, we must take care to
    # ensure the order here.
    gating_ops = sorted(set(gating_ops), key=lambda op: op._id)  # Uniquify ops.
    if not gating_ops:
      raise ValueError("Must have at least one Tensor: %s" % tensors)
    gate = group(*gating_ops)
    tpl = []