docstr: Theano like function
  
  :param inputs: (TensorFlow Tensor or Object with make_feed_dict) list of input arguments
  :param outputs: (TensorFlow Tensor) list of outputs or a single output to be returned from function. Returned
      value will also have the same shape.
  :param updates: ([tf.Operation] or tf.Operation)
  list of update functions or single update function that will be run whenever
  the function is called. The return is ignored.
  :param givens: (dict) the values known for the output
code: def __init__(self, inputs, outputs, updates, givens):
      """
      Theano like function
  
      :param inputs: (TensorFlow Tensor or Object with make_feed_dict) list of input arguments
      :param outputs: (TensorFlow Tensor) list of outputs or a single output to be returned from function. Returned
          value will also have the same shape.
      :param updates: ([tf.Operation] or tf.Operation)
      list of update functions or single update function that will be run whenever
      the function is called. The return is ignored.
      :param givens: (dict) the values known for the output
      """
      for inpt in inputs:
          if not hasattr(inpt, "make_feed_dict") and not (
              isinstance(inpt, tf.Tensor) and len(inpt.op.inputs) == 0
          ):
              assert (
                  False
              ), "inputs should all be placeholders, constants, or have a make_feed_dict method"
      self.inputs = inputs
      updates = updates or []
      self.update_group = tf.group(*updates)
      self.outputs_update = list(outputs) + [self.update_group]
      self.givens = {} if givens is None else givens
  
imports: import os
  import collections
  import functools
  import multiprocessing
  from typing import Set
  import numpy
  import tensorflow
label_desc: Use image node_id 1 to create a new _Function object from inherited base classes: object with example: obj = _Function(inputs, outputs, updates, givens)